{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext nb_black\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.animation import FuncAnimation\n",
    "\n",
    "sns.set()\n",
    "x = np.array([])\n",
    "y = np.array([])\n",
    "y_a = 1000\n",
    "text = \"Ok Let's Buy!\"\n",
    "text1 = \"Ok Let's Buy!\"\n",
    "signal = 1\n",
    "final_y = 0\n",
    "final_x = 0\n",
    "fig = plt.figure(figsize=(11, 7))\n",
    "ax = plt.axes(xlim=(0, 4), ylim=(-2, 2))\n",
    "ax.set_title(\"ETH-USDT\", fontsize=20)\n",
    "ax.set_xlabel(\"Days After COVID-19 Eradication\", fontsize=15)\n",
    "ax.set_ylabel(\"Price USDT\", fontsize=15)\n",
    "(line1,) = ax.plot([], [], lw=3)\n",
    "(line2,) = ax.plot([], [], lw=3, c=\"r\")\n",
    "(line3,) = ax.plot([], [], lw=3, c=\"g\")\n",
    "(line4,) = ax.plot([], [], label=\"toto\", ms=10, color=\"gold\", marker=\"o\")\n",
    "\n",
    "\n",
    "def init():\n",
    "    line1.set_data([], [])\n",
    "    line2.set_data([], [])\n",
    "    line3.set_data([], [])\n",
    "    line4.set_data([], [])\n",
    "    return (\n",
    "        line1,\n",
    "        line2,\n",
    "        line3,\n",
    "        line4,\n",
    "    )\n",
    "\n",
    "\n",
    "def animate(i):\n",
    "    global x\n",
    "    global y\n",
    "    global y_a\n",
    "    global text\n",
    "    global text1\n",
    "    global signal\n",
    "    global final_x\n",
    "    global final_y\n",
    "    x_a = i\n",
    "\n",
    "    if (y_a > 900) and (signal == 1):\n",
    "        y_a = y_a + np.random.randint(-18, 12)\n",
    "        text = \"Ok Let's Buy!\"\n",
    "\n",
    "    elif (y_a <= 900) and (y_a > 840) and (signal == 1):\n",
    "        y_a = y_a + np.random.randint(-6, 4)\n",
    "        text = \"Mmhh... I'm Scared\"\n",
    "    elif (y_a <= 840) and (signal == 1):\n",
    "        y_a = y_a + np.random.randint(-4, 2)\n",
    "        text = \"I knew this was a bad idea\"\n",
    "        signal = 0\n",
    "    elif (y_a <= 855) and (signal == 0):\n",
    "        y_a = y_a + np.random.randint(-6, 10)\n",
    "    elif (y_a >= 855) and (signal == 0) and (y_a <= 1000):\n",
    "        y_a = y_a + np.random.randint(-13, 22)\n",
    "    elif (y_a > 1000) and (signal == 0) and (y_a <= 1150):\n",
    "        y_a = y_a + np.random.randint(-8, 18)\n",
    "        text = \"I knew everything was going to be Ok\"\n",
    "    elif (y_a > 1250) and (signal != 2):\n",
    "        text = \"T/P, We should sell over here!!\"\n",
    "        final_x = x_a\n",
    "        final_y = y_a\n",
    "        y_a = y_a + np.random.randint(-10, 10)\n",
    "        signal = 2\n",
    "    elif signal == 2:\n",
    "        y_a = y_a + np.random.randint(-10, 10)\n",
    "    else:\n",
    "        y_a = y_a + np.random.randint(-5, 10)\n",
    "        signal = 3\n",
    "        text = \"We are Geniuses\"\n",
    "    annotation = plt.annotate(\"Ok Let's Buy!\", xy=(1, 1000), weight=\"bold\", size=25)\n",
    "    if text1 != text:\n",
    "        annotation = plt.annotate(text, xy=(x_a, y_a), weight=\"bold\", size=25)\n",
    "    x = np.append(x, x_a)\n",
    "    y = np.append(y, y_a)\n",
    "    line1.set_data(x, y)\n",
    "    line2.set_data(np.arange(0, 250, 0.1), 800)\n",
    "    line3.set_data(np.arange(0, 250, 0.1), 1250)\n",
    "    if signal != 2:\n",
    "        line4.set_data(x_a, y_a)\n",
    "    else:\n",
    "        line4.set_data(final_x, final_y)\n",
    "        line4.set_markersize(30)\n",
    "        line1.set_linewidth(2)\n",
    "        line2.set_color(\"lightcoral\")\n",
    "    ax.set_xlim(max(0, i - 75), i + 10)\n",
    "    ax.set_ylim(700, 1350)\n",
    "    text1 = str(text)\n",
    "    return line1, annotation, line2, line3, line4\n",
    "\n",
    "\n",
    "anim = FuncAnimation(fig, animate, init_func=init, frames=230, interval=90, blit=True)\n",
    "\n",
    "\n",
    "anim.save(\"test.gif\", writer=\"imagemagick\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter ffmpeg unavailable; trying to use <class 'matplotlib.animation.PillowWriter'> instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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j5wc3hw0byOnTp/Ls97ffLqNGjZpX3Ldr1w7n63bt2jtft2/fkRUrljqPyT1M/+F8nfXLxw03dOOGGzK+WAQFVSAqKo62ba/Hw8ODyMhIGjW6FJiz9u/yaRuX++23dc6R7ptu6uUSpDM1b96SBx8cQ0JCPE2bNsPPz3WaR3JyMgsWfMmaNT9y8uQJrFYrjRs3ZciQYfTqdVuun5/VwYMH+eSTT9i4cSNnz56lSpUq3HDDDYwdO5a6des6jzt+/Di9evUCYOLEidxyyy3885//ZOvWrdhsNnr06MGzzz5LtWqXAn9gYKDzdZUqef//RWFaRETEDe22ncG8Ut57+wboFgFdj7tstpoW9tvOllmYXrDAAy8vk0GD8v8FIKtTp046X/v7+wMQFxfH/v17AWjf/nqX4994Yzo//LDKZdvZs1GsWfMTW7eG8uGHc12Cavv2HViw4Cuio6M5fPhQriO0xSEiItz5umrVIOfroKBLr48fP5ZrG2Fhl6buJCQk8Mgjozly5DA1a4Zwzz33MWDAYOf+3r370Lt3n2xtbN++1fk6p+Cf6ddf1zlf5xZ8R4164Irbz58/x8SJj3HkyF8u27dtC3X+N2PGP3LtA8CPP/7IM8884zLX/PTp0yxatIjVq1cze/ZsunbN/ozA7t27mTNnDklJSc5ty5cv58iRIyxatMi5rWrVqs7XWf/3yInCtIiIiBuyc4WpEhe84Lne4JUOXy2EIQey7DRJI+8pISUhNRUWLbJx++3pVKqU//McDgexsbGsWfODyzSFdu0ygvO2baHY7RkPXTZo0Mi5Pzr6rDNIN23anDvuuBMfHx82bPiVH35YRUzMeT7//DOef36K85z69Rs6X2/fHpprmH7uuSkkJ+c9wh4YWDnHfVlHgbM+pOfj4+N8nfWhwis5f/4ckFH1ZOrU50hISADg6NEwZsx4lfPnz/HUU0/keP7BgwdYvPhbAGw2G9269cj187KOxl8+sp8fb731D2eQbtGiFQMGDCYu7gJffjmPmJgYFi9eSIcO7Rg2bFiObZw6dYpJkyaRkpKCYRjce++9tGnThi1btrBw4UISExOZOHEiq1evpnJl1/u/du1amjRpwujRo4mOjubdd9/F4XCwd+9edu/eTatWGStvZh2NVpgWERG5RtV0VOCMNcF1Y9jFpOqXCsPugtnfw6OXRh5r2SuUYg8vWbvWSnS0hbvuyt8Uj9dfn87rr0+/4r5+/QZSr159IGPecaaso8xZRx4bNmzEzTf3ws/Pn169enP99Z2oXr2GS3gGqFkzBKvVit1uJzz8aK7969SpS76uIzepqZdGVa3WS3N0s87XzTryeiWZgd40TdLT0xkzZhwVKgQwd+5/OXcumo8++g/Dhw/B2zv7N5iIiKM888xEZxtDhw53GSG/ktjYGOdrLy9vl30vvPC0y8h1pgcf/BsPP/woERHhzv316jVg1qz/YrNlxNBOnbry8MP3YbfbmTNnTq5h+uuvv3Z+aXjyyScZOzZjfvsdd9xBxYoV+fjjj4mNjWXBggXOfZl8fX357LPPnNM4jh496hyRPnbsmDNMZx2ZDg4OzvWegMK0iIiIWxqT1J5nrD+RYMkybSLs4lzPxd/AmzfC2IFwxh9eXIef6Umn9JAS71cSaSzx+pOPfbZzyhKPDQuOxcOpGBTCTTenQyEXkfH09GLo0OHOh+wAYmLOOV9nTv0AqFWrNk2bNufAgX18//0yfvhhJU2bNqd167Z07tyVNm3aZXuwzDAMfHx8iY+PIybmfK59KY450x4el+Z32+2X/mKQ+XAfgJeXV67tZ50jfv/9DzN69ENAxmjqpElPY7fb+emnnxg4cLjLeeHhR3niiUedVVEaNWrMmDGP53k9lSpV4tixCCBjik1BZJ1O0r//IGeQzvj862jRohW7du0gPDycqKioHEeEN23a5Hx99913u+y75557+PjjjMouW7ZsyRammzVr5jIfun79+s7XWb+4ZI5MG4ahOdMiIiLXqgEp1zHJf43rxsyR6RaRsORr+NsgmHYz1lMVeGxGHJYSXg3xZ4+jPBywHBPzUsg/5wPfh2B7bCt9grbyTeydBJm+ubYzbNjdtG/fEYvFwMPDk0qVAqlTp67LFAiAuLhLUyW8vV33vf32v5g1613Wrv0fqakp7Nmziz17dvHll/MICanFiy++SsuWrVzO8fHxIT4+jsTExCLchfzJGv5TUi6N2GcdVc8sW5eTrPtbtWrjfN26dTvn6/DwcJdzjh8/xhNPjHU+SFirVh3eeec9Z0m73ISE1Gb37owKJH/9dcilDvZ99z1I374ZC79s27aFhQu/cTn3woVY5+srBeWs22JjY3MM0+fPZ3zR8fT0dAnGgMtDhLGxsVwuIMD1fmb9MmKal6ZN+fv74+3tjZ+fn0voz4nCtIiIiBvywsb8C3dwd8XvSDIuLs0dFggVUqByUsYA8Nwl2IITSX/7BrYcT4Ub5lD5w39iOXkSR7VgEh97guSHx5CvFVTysN4jnPsrLiXZuGyZ8G9aQKqN9Pu3c8B2lj6BX7Dm/CgqmTmHt8tL4+UkayBNS0tzGW0ODAxk6tTpPPXU82zbFsqOHdvYsWMbBw7s48SJ40yZ8gyLFq10OScpKSNE5xVii2POdNZpKWfPns3yOsr5Oq8FaOrWrcfu3TsBXPqTNQBmvb6YmBieemo80dFnL7Zfh/ff/4/LtIbcdO/ek9Wrvwfghx9W0r//IOe+Fi1aOl/Hx2cftc4afKOiorLtz7rt8rnOWVWpUoWjR4+SmprK+fPnXdrNWuXlSm1YCvBzXqVKFSpUyN+0qKti0Zb4+HgGDBjA8eMZc582bNjAwIED6d27NzNnznQed+TIEUaNGsWgQYN4+OGHnd86Tp48yciRI+nTpw+PPfaYcy6NiIjItaxLWi2+jRlGsN0Pf4dHxsh0/fNggJdpxQsrd04/zrRXkvh+hSfDp7YmPvwcRloq1uPH8X/tZSo89kiR+5GGnUcClmcP0gDz2kDLM9D2NOmGgzOWeF7z+7XInwmuYTXrfN4DB/bxwQfv8swzT7Bjxza6devB+PFP8tFH8+jS5QYAoqOjXSqEOBwO54h0bmEOMuZM9+hxU57/5Tbam1nnGTJGcjPt3HmpZF7W0eYryXwQE2DDht9crj9TgwYZtcVN02T69CmcPHkCyJgj/sEHHxIUlPec4ExdutzonLaybVsoCxZ8le2YlJRkNm/emG17ixatna9XrVpOevqln5UjRw6zd+9uABo2bJjr/W/btq3z9bfffuuy75tvLo2Gd+zYMa/LydXatWtZunRpvo4t85HpnTt3MnXqVI4ePQpkfLOaPHky8+fPp0aNGjz66KOsW7eOHj168NhjjzFlyhR69OjBO++8w5w5c3j22WeZPn06I0aMoH///syaNYvZs2fz7LPPlu2FiYiIlIJO6SHsOvcov3gc5ZEj1bA0PE/TtJp0T63DA8ltqObwx3rLXhq9OosH0+bQk3Wsoi81OI2RlIjX6u9J2rWD9NZt8/6wHKz2/Iv0K1UKOVgFNtaGt390TpVONRx8672P6fE34YdHoT8TMubaZjp58gTVqlUHMkYgv/76cwAOHNjPiBGjqFatBuHhYezYsQ3ImINduXIVl/Mz5ys3bty0SP3Kj4YNG9GwYSP++usw33+/jFq16mCaJitWLAEywnbWken1638BMkbj27fvAECPHjdTpUoVoqOjWbJkIZ6eHtSv35B58z5xXuPtt99+8fyf2bLl0nzj227rw759e136FBJSi4YNG5ETLy8vJk16kb///XEcDgfvvfdPNm/+g549b8HHx4fDhw+xatUK58g3XBoNrl+/Ae3bd2DbttCLS5ePYcCAQVy4EMeXX85zVmW5fJ7z5YYPH87nn39OSkoKM2fO5MyZM7Ru3ZrQ0FAWLFgAZHwZuuuuu3JtJzcREREcPJixGmTz5s2pWTP3koFlHqYXLFjAtGnTeO65jLXjd+3aRd26daldO+MHaODAgaxevZoqVarg6+tLjx4ZZVvGjh3LhQsXSEtLY8uWLcyaNQuAoUOHct999ylMi4hIuWHB4ObU+jiO+jOyuy+vxtzrst9z9feMNL8gmBMMZRE3sIGNdKEakZCSgufypUUK05/67CTecoX60fNbg8UBI3a7bLaaFn7y+os7UooWWlu2bO2swLF//z7nSG3jxk0ZPfoh5s37hJiY88ye/V62c8eOfRxf30tztw8fvrSUdtZR45L09NOTePLJx0lNTWHWrHed2z09vZg48RmXYydPznjfqFFjPv30SyBjjvfkyS8zadJTpKWl8c03XzqPNwyDiROfJigoiKioOL77boFLe5lLsGc1fPi9TJz4dK59vv76jrz55kymT59KfHwcGzduYOPGDdmOs1qt9O8/iLvvHuHcNnXqdCZOfIxjxyKcc9izuvvuEQwaNOjyplzUr1+fV199lSlTppCWlsbnn3/ust/f35/33nuPihUr5tpObtauXcsbb7wBwBtvvMHQoUNzPb7Mw/Q//uFanDsyMtJl0nlwcDBnzpwhIiKCqlWrMnnyZPbv30+DBg148cUXOX/+PP7+/s75QUFBQS5zZkRERMqDs2cNEhMN6tTJPkJspKeDw05vfmINvejCJj7kUV7iVTBNjPTCLaSS6bQlh5XzvmoFtx6Bmq5zaFMNO5GWoj/k5+vrS+vWbdm+fSs7dmxlxIhRzn1jxoyjefMWfPfdAo4eDePcuWj8/f1p0qQZd955Nzfe2N2lrQMH9gNQr159Z+m9kta6dVtmz/6IOXNms2fPLkzTpHnzlowZM85lDnJuOnfuyr///TEff/whu3btID09/eKXiQedy6unp6ezZ8/uPFrKv65db+SrrxaxatUK1q//mVOnTnLhQiwBAQHUqVOPTp26cPvt/bItIx4cXI25c7/k22+/Ys2anzh+PAKr1UqTJs0YNuwebrrp5nx9/uDBg2nWrBlz587ljz/+4OzZs1SuXJlu3brx2GOPOQdkS4thZn18sQzdcsstzJs3j23btvHrr7/y9ttvA/D777/zySefMHjwYF588UU+//xzWrVqxbvvvsvp06f5+9//zl133cW6dRm1C9PT02nXrh27dxffD42IiMjVbtMm6NIFli2DgQMv27llC9x0E1ycE3wrP/EXDfmLhlj8/WDFCujZs9Cf3ZZ/s5PLBrKSbeAzFV5ZCy+ud9nlg42Z9OFROhT6MzMtX76cZ555Bn9/fzZv3pyt5F1+3XnnnezZs4enn36aMWPGFLlfUn6U+cj05apXr+7yRGdUVBTBwcEEBQVRt25dZ0HtAQMG8MQTT1C5cmXi4uKw2+1YrVbn8QURHR2Pw3FVfKfIU1BQBaKiClbbUfJP97dk6f6WHN3bkuUO93fnThvgQ8WKCURFXTY6Xa8pFTt1wWPjBozkZB7mY0bwFWs8buem5jHENGsHRbi+Ln4h7POJIs3I8rkRF//MXi8m+wkmND4fSJQ94zOLcn/bt7+BSpUCiYk5z48//kKHDp0K3MbJkyfYu3cvnp5edO9+61X/v3VBuMPPbiaLxaBKFf+8D7zKXBXVPLJq06YNYWFhhIeHY7fbWbFiBT169KBdu3acO3eOAwcylkZdu3YtLVq0wMPDgw4dOrBy5UoAlixZ4pxXLSIiUl5ERGT8Sq9d+8pLhsfO/4ak0Q/i8PVlsOdqKnGej2tPI2bBEjCKVn/6kaT22WtYH71Y8/oKYbqevRIt7QUb+MqJp6cnDz2UMZK8fPniQrWxfPkSTNNk+PB78lwFUORyV12Y9vLyYsaMGUyYMIF+/frRoEED+vTpg7e3N7NmzWLq1Kn079+fTZs2MWnSJACmTZvGggUL6NevH6GhoTz55JNlfBUiIiKlKyLCoGpVB/45Dex5eZHw2ptEHzpG4rZQht7vw5ITnYlJzX0Blfyo66hI75QGeDuy/ME7hzDtY9qYmuA6X7moBg8eSv36Dfjll7VERhbsuank5GSWLVtEpUqVGDXqwWLtl5QPV82c6bKkaR6SSfe3ZOn+lhzd25LlDvd32DAf4uMNVq/O34N9u3ZZuPVWP2bMSOahh4r2ACJkLCM+rNJC9lgjSbKkw+Re8NSvcSMAACAASURBVPYNkPwaWDN+x/qYNqbEd2dMcnuXc93h/rord7q3muYhIiIiZSYiwnLFSh45ad3aQcuWdr76qmi1njP54MGSmLt4NvEGguy+2MICMepcoILhgZdp5fq0GsyLvSNbkBZxd1fdA4giIiJSMHY7nDhhMGhQ/sM0wIgRaUye7M2ePRZatizYuVfigZXxSR0Zl9SBnn/ZoFYaz8XdTgt7EA3sgXk3IOKGNDItIiLi5k6dMkhLM6hTp2BTFu+8Mw1PT7PYRqczWTCIifDm+lo+DExtrCAt1zSFaRERETeXWcmjINM8AAIDoV+/dBYu9CAlpfj6k5wMZ85YqF3bPZ5HEikKhWkRERE3FxGRUZauoGEaMqZ6nD9vsHp18c38PHEioz85lekTuZYoTIuIiLi58HALhmFSq1bBR4K7d7dTq5aDL74ovqkel0bKNTIt1z6FaRERETcXEWGhZk0TT8+Cn2u1wt13p7FunZXjx4u2eEumY8dyX0BG5FqiMC0iIuLmIiKMQk3xyHTvvWmYpsHXXxfP6PSxYwY2m0n16hqZlmufwrSIiIiby6gxXfjgWqeOSffu6Xz9tQeOYhhMPnbMQkiIidVa9LZErnYK0yIiIm4sJQVOny7ayDTAyJFpRERY+O23oifggi4gI+LOFKZFRETc2PHjBqZZ9DDdt286FSuafPll0ad6HDtmaL60lBsK0yIiIm4sPDzjV3ndukWbn+zjk7GIy/ff24iJKXw7qjEt5Y3CtIiIiBsr7IItVzJiRBopKQaL56VgPXwoIxkXkGpMS3mjMC0iIuLGIiIMPD2Lp3JG2wqHaeN7kG/+EUGl23pQpVl9fN94Fez2AvRHNaalfFGYFhERcWMRERZq1TKxFPE3unEumsC+t/BQ0gdsM9uzK6ERloQEfP8zC7+pk/LdjmpMS3mjMC0iIuLGiqtyhve8uZCYyH3m53iSwic8BICRlIjP559iREfnqx3VmJbyRmFaRETEjRV1wZZMXj+swpKcTGXOM4TFfM59pJNRJs/09MQjdHO+2lGNaSlvFKZFRETcVHw8nDtXtAVbMpk+Ps7XA1jBeSrzJ00u7ff2zlc7qjEt5Y3CtIiIiJu6VBav6OE1ecQoHL6+ALRhJwA7aePcn9b1xny1oxrTUt4oTIuIiLip4iyLlzJ4KPYWrTC9fWjKATxIZSetMX18iH/nX+DpmWcbqjEt5ZHCtIiIiJuKiMio6VwsZeg8PIhZtIKESVOx1K1Fc8uf7Ai8mZhvlpAyZFi+mlCNaSmPFKZFRETcVESEBT8/k8qVi2kk2MuLpHETOLdlF02GNWWnZ0fSu3QtUH9ANaalfFGYFhERcVOZD/sZRvG33aKFnTNnLJw9m//GVWNayiOFaRERETcVEWEUy8OHV9KiRUa7+/blPyqoxrSURwrTIiIibsg0M6p5lNSUiubNM8L03r0FCdOqMS3lj8K0iIiIG4qONkhMLJ4FW66kalWTatUc7N2b/2SsGtNSHilMi4iIuKFLlTxKLry2aOEo4Mi0akxL+aMwLSIi4oZKo3JGixZ2Dh60kJqa97GqMS3llcK0iIiIGyrOBVty0ry5g7Q0g8OH844LqjEt5ZXCtIiIiBsKDzeoUsWBv3/JfUZmRY/8TPVQjWkprxSmRURE3FDGw34lG1wbNXLg5WXm6yFE1ZiW8kphWkRExA2VRuUMmw2aNMnfQ4iqMS3llcK0iIiIm7Hb4fjxkiuLl1Xz5o58LdyiGtNSXilMi4iIuJnTpw3S0oxSmZ/cooWdqCgLkZG5LyuuGtNSXilMi4iIuJnSqOSRKb8PIarGtJRXCtMiIiJuJjy85BdsydS8uR3IPUyrxrSUZwrTIiIibiYiwoJhmNSqVfLhtXJlqFHDwb59OU+GVo1pKc8UpkVERNxMRISF6tVNvLxK5/PyWlZcNaalPFOYFhERcTMREaVTySNTixZ2Dh2ykJJy5f2qMS3lmcK0iIiImymNBVuyatHCQXq6wcGDV44NqjEt5ZnCtIiIiBtJSYFTp0p3ZLp584zPyqnetGpMS3mmMC0iIuJGTpwwME2DunVLL0w3aODA2zvnZcVVY1rKM4VpERERNxIeXvoP+9ls0LRpzg8hqsa0lGcK0yIiIm6kNBdsyap5czv79lkwL8vwqjEt5Z3CtIiISIlKwcNjLZ6ei/Hw+BVIL1JrEREGHh4mNWqUbnht0cJBdLSFM2dclxVXjWkp72xl3QEREZFrUyq+vq/h4/MRkDWA2khMnEhS0pMUZkwrIqJsHvbLXFZ83z4L1avbXfoDqjEt5ZdGpkVERIpdKpUqDcDX90Mslngslrgs/53Hz+8tAgLuBwo+mltWD/tlLiu+Z49rileNaSnvFKZFRESKma/v29hsOzGMpCvuN4xEPDx+wtv7iwK3HRFRupU8MlWqBCEhjmzl8VRjWso7hWkREZFilYaPz39yDNKZLJZEfHzeAfIfQuPjITq6dBdsyepKy4qrxrSUdwrTIiIixchm28bl0zdME3777Ubsdtdfu1brKSyWY3m2aU04jP+BZ4hZdg8A19k+x5IYVmx9zq8WLewcPmwhOfnSNtWYlvJOYVpERKQYWSyxuD5wCN99dyfdu/9Gnz6riYwMcm43TQ8MIy7X9rxOzCNw4414H5/LsYiM4d8m1k+o/EcXvE59U+z9z02LFg7sdtdlxVVjWso7hWkREZFi5HBUA+wu29av74GnZwq//daNtm138Ouv3QAwjFQcjqArtJLBFruFCgeexXAkYZhphEXVB6BB1UMYjiQq7HsCa9yuEruWy2U+hJg51UM1pkUUpkVERIpVenprTLOyy7aNG7vQtesfbNzYBX//eG6++WfeeutZUlPbYiQm4nfgWSqva0SVn+tQcXNvPCO/B9OB75F3wHFpTkVYVH18vRIICojK2OBIwTdsZqldW/36Jj4+l5YVV41pEYVpERGRYmaQkDAJ0/QFICnJm+3b29G16x+0abOL0NAODB26iOeff4s7+n4GP/bF5/gnWFMjsaTH4Bm7kQp7HiFg5yg8z63FuPiA4sFT17Hh4A3UDwrDMDI/yYHn2R9K7cqsVmjW7FJFD9WYFlGYFhERKXYpKfeRlHQfpunL1q3Xk57uQdeufwAQEBDH118/wNtvLGXN+vpc/8JvhB5u43K+xZ6AZ/T/+PN4fV5bPIU2L+ygyTMH2XKkE/3bfu9yrGEWbUXFgmrRws7evVZMUzWmRUBhWkREpAQYJCS8w4ULH/Hbb8MA6Nx5N6bpSWpqDy5cWMDYW3aw7uXbME2DG6f/zgc/Po5pwoGTTXh18VTaPLeRZs/u58WFr+HvFc/M+54k4r3avHnvJJdPSvdrVqpX1ry5g/PnDU6dMlRjWgQtJy4iIlJiUlMH8Ntv3tSrl4bNtozo6EBMsyIAflGv0LXBFra/3o7R/57HhM8+4M3lz3P8XG0Abmz8GzNHPc2dnZdSO/CvK7ZvWv1IrP9kqV0PXFpWfO9ei2pMi6AwLSIiUmJME0JDrXTrZsfhqHfZzozKGJX9z7Ps6UHMXPV31uztxbP93+bOTt8RUvkkDosf6QHtMC+cxHC4LgJjWnxJrXIrqcGDS+lqMlyq6GFVjWkRroJpHvHx8QwYMIDjx48DsGHDBgYOHEjv3r2ZOTP7E8q//PILt9xyi/P9hQsXGDNmDH379mXkyJFERUWVWt9FRERyc/y4wZkzFjp0sGfbl1q5J6bhCYDFYvJ0//9j5XP9eaLP+4RUPplxkGEQ234hCY1exO5ZDdPijWnxwu5Vg/jrpnOh9WdglO6v8oAAqFMn4yFE1ZgWKeMwvXPnTu69916OHj0KQHJyMpMnT2b27NmsXLmSPXv2sG7dOufxZ8+e5c0333Rp491336VDhw6sWrWK4cOH849//KM0L0FERCRHoaEZ8x86dsweppNr/y3XIGxavEmq9RBYfUmqO55zPf7kXLddRHfbw7nuB0iu82ipB+lMzZvb2bbNqhrTIpRxmF6wYAHTpk0jODgYgF27dlG3bl1q166NzWZj4MCBrF692nn81KlTGT9+vEsbv/zyCwMHDgRgwIABrF+/nrS0tNK7CBERkRyEhlrx8TFp1iz76K3DuxZxzWZiWny4PI6aFh/S/ZuR2HDKpY2GBYdXdUyvajhr45WR5s0dzrJ4GpmW8q5M50xfPoocGRlJUNCllaCCg4M5c+YMAPPmzaN58+a0adMmx3NsNhv+/v6cO3eOatWq5bsfVar4F/YSykRQUIWy7sI1Tfe3ZOn+lhzd25JVmPu7cyd07Ag1a+ZwbtBYqNkKdr8CZ9aA6QCfGhhNn8Kj8eMEWb2L2OsS8Msv3PDVd/wf7wPQevp9BFUfAXfeWaRm9fNbcnRvS9ZV9QCiw+HAyPJt2zRNDMPg4MGD/Pjjj3z66aecPn061zZM08RiKdiAe3R0PA6He/yZKiioAlFRcWXdjWuW7m/J0v0tObq3Jasw9zc5GbZv92fs2FSiolJzObI1tFoILR0ZDyVaPDI2n0sDrq6/tNq2hVJpSH/aJNV0bqsXtRlz1CouJKSR2rd/odrVz2/Jcad7a7EYbjfACVfBA4hZVa9e3eUBwqioKIKDg1m9ejVRUVHceeedjBkzhsjISEaMGAFkjF6fPXsWgPT0dBISEqhUqVKZ9F9ERCTTzp1W0tIMOnTI5zQIw3IpSF+l/F6bDklJNOAIfsRjI42anMRISsJv2uSM8iUi5cxVFabbtGlDWFgY4eHh2O12VqxYQY8ePXjiiSf44YcfWLp0KXPmzCE4OJgvv/wSgJ49e7JkyRIAVq5cSYcOHfDwuLr/MRIRkWtfaGjGr9jrr8/+8KG78tj8BwZgwaQVu6nNMaxkfFmwnjiOEXO+bDsoUgauqmkeXl5ezJgxgwkTJpCSkkLPnj3p06dPrudMnDiRSZMm0b9/fypUqMA777xTSr0VERHJ2datVurUcRAcfO2M1po2D4zUjCkrr/IiMWT5S7BpYnp4llHPRMqOYZr6m4zmTEsm3d+SpftbcnRvS1ZB769pQps2ftxwg53//Ce5BHtWuvwnPIb3d99gpKe7bDeBtC43ErtsVaHa1c9vyXGne6s50yIiIgLAiRMGp09brlhf2p0lTn4Rs2IlTNul6ZSm1Yrp70/8DP1lWMonhWkREZFilrlYy5VWPnRnjho1Of/LBpJHP4CjSlUcgYGkDB3O+f/9ir15i7LunkiZuKrmTIuIiFwLMhdrad782lvQxFGtOvEz/kn8jH+WdVdErgoamRYRESlmW7daadPGjopLiVz7FKZFRESKUXIy7NplueameIjIlSlMi4iIFKNduywFW6xFRNyawrSIiEgxulYfPhSRK1OYFhERKUbX4mItIpIzhWkREZFiFBpq1ai0SDmiMC0iIlJMTpwwOHVKDx+KlCcK0yIiIsVE86VFyh+FaRERkWISGmrF29ukRQtV8hApLxSmRUREcmUH8heOQ0O1WItIeaMwLSIichnDiMXH5wMqV25K1aqVAQ8CAzvj5fUVkHrFc1JSYPdui+pLi5QztrLugIiIyNXEYomgUqXbMIwYLJaki1tNbLb9+Ps/hY/PHGJilgP+Luft2mUhNdXQfGmRckYj0yIiIk7pVKrUH4slMkuQvsRiScBm20NAwAPZ9unhQ5HySWFaRETkIk/PlRhGNIaRcyA2jBQ8PddjtR522Z65WEu1alqsRaQ8UZgWERG5yMfn31gs8fk40o6X12cuW0JDrVx/vUalRcobhWkREZGLrNZj2bbdeutPtGy5m88/H0l6esZUDsNIw2a7ODJtt3NqVzQnT2qxFpHySGFaRETkItP0dnl/6FAj1qy5lZMnazJq1Oc0afIn//3vI6SkeGI6/PB5/12qNGvAgT5TAOi5agpGZGRZdF1EyojCtIiIyEUpKYMxTS/n+4ULhwGwY0dbliwZTJUq0YwZ818aNjzCB71vgXf+hSXmPBvTr8ebJDr88W8Cb78JEhLK6ApEpLQpTIuIiFyUnPwIYDjff/vtcLp0+YM6dY4xePAyNm3qzI8/3kajOmE8+9ujNEjaxxtMYh096UAoXvYkjPPn8F74ddldhIiUKoVpERGRixyOGsTHv4lp+vDXXw3Yvr09w4YtdO43DLj11t/5/tFVrPPpzfVsZTJvsI3r6cJGACyJiXgtWVxWlyAipUyLtoiIiGSRnPwgDkclFiyIAODOO5cD3pimBbu9DnFxH2Axouhme59V9GMr7ZnLg/yN/15qxFPriYuUFwrTIiIil0lNHcKCBT60b3+eKlX+BvgSE9Oa9PS2ABjd4zDS0wG4nm1czzbnuQ4/P5LvGVkW3RaRMqBpHiIiIpcJDzfYscPGgAE+JCePBR53BmkA078C8VOnY/r4upxnentjv64JKQMGl3KPRaSsKEyLiIhcZvnyjD/cDhyYnuMxyX8by4X/fExai5aYnp44qgaROO4JYpasBA9N8xApLzTNQ0RE5DIrVnjQpo2dunVzXxo8tW9/Uvv2L6VeicjVSCPTIiIiWRw7ZrBtmzXXUWkRkUwK0yIiIlmsWJE5xSOtjHsiIu5AYVpERCSLZcs8aNXKTv36uU/xEBEBhWkRERGnEycMtm61MmiQpniISP4oTIuIiFykKR4iUlAK0yIiIhctW+ZBixZ2GjTQFA8RyR+FaREREeDUKYMtW1TFQ0QKRmFaRESES1M8Bg3SFA8RyT+FaRERETJWPWzWzE6jRpriISL5pzAtIiLl3pkzBps2aYqHiBSclhMXEZFy6bD1HPusUaQZDravrINp+qsknogUmMK0iIiUK2s8w3jT93cO2KKxmRbAJHHlfVibn2VNm900SGqPTX+4FZF80r8WIiJSbrzvs5mHApaxw+MMyUY68ZZU4iO9cKyvg334Hmb4/s5dFReSgkaoRSR/FKZFRKRcWOF5kHf8/iDJuCwoL24KpgHD9pFkSWerxyn+XuHHsumkiLgdhWkREbnmmZi84vdr9iAN8G0LaBoFLSIBSDLSWe51kFOWuFLupYi4I4VpERG55m21nSLSmpB9R6QfrKsLw/eB4bprrvfO0umciLg1hWkREbnmbfM4TTqO7DsWNwWHBYbtc9mcYtj51TOilHonIu5MYVpERK55yaTjuFKY/qUe1I6FVmey7UrBXvIdExG3pzAtIiLXvGCHH17mFarBHq4MzaKyTfEAqOnwL/mOiYjbyzNMnzx5kvj4+NLoi4iISInom9oQu3HZyLQJHKoCjc5lO97f4cHo5Nal0zkRcWt5hulevXrx2WeflUZfRERESkRF05uByU3wMLP82jvnA7HeVwzT3njQK7V+KfZQRNxVnmHaNE1M0yyNvoiIiJSYVxJ6UtXhe3HVQzKmeEC2MO1j2vjkwkCsmgkpIvmgfylERKRcqGL68mPMSBrZK+Pn8IBDrmHaz+FBgMOTL2OH0iWtVhn2VETcyRWexhAREbk2VXP4s+78aDZ6nOCpP1P5yzAJqpNCSFo1/pbUnoEpjfHWr0YRKYB8/Ytx4MABlixZUuDG77jjjgKfIyIiUpIMDLqm1aLdAW9SQky2JY6BxLLulYi4q3yF6TVr1rBmzZp8N2qaJoZhKEyLiMhVKyzMQv36V6g9LSJSAPkK0x07dqRTp04l3RcREZFSc/SoQb9+WphFRIomX2G6U6dOjB8/vqT7IiIiUiouXIDoaAv16qlalYgUjap5iIhIuXP0aMavP03zEJGiUpgWEZFyJyxMYVpEisdVEabj4+MZMGAAx48fB2DDhg0MHDiQ3r17M3PmTOdx//vf/xg8eDCDBg1i3LhxxMbGAhlLno8cOZI+ffrw2GOPkZCQUCbXISIi7iEzTNerpzAtIkWTZ5ieN28eQ4YMKbEO7Ny5k3vvvZejR48CkJyczOTJk5k9ezYrV65kz549rFu3jvj4eF5++WXmzJnDsmXLaNKkCe+//z4A06dPZ8SIEaxevZqWLVsye/bsEuuviIi4v7AwC9WqOfDzK+ueiIi7yzNMd+rUiZCQkGzbExMTiYyMJD4+vkgdWLBgAdOmTSM4OBiAXbt2UbduXWrXro3NZmPgwIGsXr2atLQ0pk2bRrVq1QBo0qQJp06dIi0tjS1btnD77bcDMHToUFavXl2kPomIyLUtLMzQFA8RKRb5XubJ4XCwdOlSvv/+e7Zt20ZSUpJzX8WKFWnfvj0DBw6kT58+GIaR7w784x//cHkfGRlJUFCQ831wcDBnzpwhMDCQ2267DcgYvZ4zZw6jRo3i/Pnz+Pv7Y7NlXEpQUBBnzpzJ9+cDVKniX6Djy1pQUIWy7sI1Tfe3ZOn+lhzd2/wLD4c+fQp2z3R/S5bub8nRvS1Z+QrTJ0+e5NFHH+Xw4cOYponFYiEwMBBPT08SEhKIiYlh7dq1/Pzzz/z3v//lvffeo1atWoXqkMPhcAnjmQvAZIqLi+Pxxx+nadOmDBkyhDNnzmQL7wUJ8wDR0fE4HO5RHikoqAJRUXFl3Y1rlu5vydL9LTm6t/mXkACnTlWgRo0UoqJS83WO7m/J0v0tOe50by0Ww+0GOCEfYTouLo6RI0dy6tQpevTowUMPPUT79u3x9PR0HnPhwgVCQ0P54osv+P3333nwwQdZsmQJfoWYjFa9enWioqKc76OiopxTQCIjI3n44Yfp0qULkydPBqBy5crExcVht9uxWq0ux4uIiFxOZfFEpDjlOWd67ty5nDp1iscff5w5c+bQpUsXlyANEBAQwC233MLHH3/MU089xbFjx/jiiy8K1aE2bdoQFhZGeHg4drudFStW0KNHD+x2O2PHjqVv375MmTLFOfrs4eFBhw4dWLlyJQBLliyhR48ehfpsERG59qksnogUpzzD9Nq1a7nuuuuYMGFCvhocM2YMDRo0YM2aNYXqkJeXFzNmzGDChAn069ePBg0a0KdPH9auXcu+ffv44YcfGDx4MIMHD2bKlCkATJs2jQULFtCvXz9CQ0N58sknC/XZIiJy7VNZPBEpTnlO8zh+/DgDBw4sUKMdO3Zk1apVBTpn7dq1ztddu3Zl2bJlLvtvu+02Dhw4cMVzQ0JCmD9/foE+T0REyqejRw2qVnUQEFDWPRGRa0GeI9NJSUlUrFixQI0GBgYWuWSeiIhISQgLs1C3rns8dC4iV788w7TdbneWncsvm82GaeofKhERufqEhVk0X1pEis1VsZy4iIhIaUhOhpMntWCLiBSffA05x8fHc/LkyXw3euHChUJ3SEREpKRERFgwTYVpESk++QrTn332GZ999llJ90VERKREhYVllFVVmBaR4pJnmO7YsWNp9ENERKTEqca0iBS3PMO0Ss6JuBsTD49f8PL6GoslCoejOsnJo0hP7wIYZd05kTIVFmahYkWTwMCy7omIXCsKVqZDRK5qVutBKla8E8M4i2EkYBhgmgZeXotxOGoRG/sdDkedsu6mSJnJrORh6HuliBSTAoXpEydOEBgYiK+vr3NbZGQkCxYs4OjRowQHB3PHHXfQuHHjYu+oiOTOYgmnUqVeGMYFDONSaUrDMC8G68MEBt7MuXN/YJrBZdhTkbITFmahfXt7WXdDRK4h+SqNd/DgQYYOHcqtt97Kli1bnNv379/PoEGDmDVrFitWrOCTTz5hyJAhfPHFFyXWYRG5Mj+/aRhGnEuQzsow7BhGDL6+b2dsSE3FY+3/8FryHZaI8FLsqZRPDjw9V1OxYl+qVKlB1arVqFTpRry8FgCppdKD1FQ4dkyVPESkeOU5Mn3u3DlGjRpFbGwsbdu2pXLlygA4HA6ee+45YmJiaNOmDS+88AKmaTJz5kxef/11WrVqRevWrUv8AkQEDOMcXl7fYxi5hwTDSMPbez5pS7pT4bHHwXSACUZaKqm39ubC7I/Ax6eUei3lRzIVKw7HZgvFYklwbvXw2I3VOhFf338SE7MS06ySscM0wZEMhg0sHsXWi+PHDRwOg3r1FKZFpPjkOTI9d+5cYmNjeeutt/j6669p1aoVABs2bODQoUN4eXnx/vvv07ZtW9q1a8fs2bMJCAhg3rx5Jd55Eclgs+3HNL1ctp06VZ0HHpjLSy9N57vvhnL4cEMcDgP2QsAjj2C5EIslLg5LfBxGSgqea36iwlMTyugK5FoWEPA3bLZNLkE6k8WSgNV6mIoVB4EjBZ/wD6i8vglV19ak6pogKm6+DY/otcXSj0uVPLRCr4gUnzxHptetW0fHjh0ZNGiQy/aff/4ZgG7duhEcfGn+pb+/Pz179mTjxo3F3FURyVn2OaDTpk1n/vxRADgcVgD8/eNo5bOH1sk7acsO2rCTNuzElySM5GS8Viwl/pU3MIOCSrX3cu2yWP7C0/MHDCM5x2MMIw0rfxG4tTuWC+FYHEnOfZ6xm/DYcS/xjV4iue7jReqLyuKJSEnIc2T6xIkTNG3aNNv2TZs2YRgGN954Y7Z91apVIzo6unh6KCJ5stsbYxgpzvdHjtRn7twHGTduNvHx/mzZ0oGPPnqYBx74FFt8Gl+b9/AY/+EG/qApB4ihIgCmlxe2P/eX1WXINcjHZy5X+rJ3OWN/ItYLB12CtHOfIwn/w69gjf+zSH0JC7Pg62sSFKSRaREpPnmOTDscDmw218Oio6M5fPgwhmHQpUuXbOfExcW5VPwQkZLlcFQnNbUbnp5rMAx45ZWXsNnSeeGFN/DxSaZDh6106LAV07Tg2F4Fy+9RhFOX9fTgfubxT57mVV7CSE/HUaVqWV+OXEOs1gMYRprLtilTXuPjjx+mbt1w6tcPo369MOqdP0r9wDDqB4dRt2o4njbXc3Ck4RMxi/jm7xW6LyqLJyIlIc8wXbNmTY4ePeqy7ZdffnHua9CgQbZzNm/eTEhISLF0UETyJyHhFTw9/+DgwRDmzx/FxIn/ombNUy7HmKYfiQ89i//2l6mXGE495vM9/ZnJ3xnP+1SpWQl702ZlgQKWqQAAIABJREFUdAVyLTJN78vew6efPkDFirEEBFxg69brWbRoKGlpns5jDMNBSOAJ3rzneUbc+FXGNtLxOP97kfoSFmahWTOVxROR4pXnNI+ePXvy66+/smvXLgBSU1OZN28ehmEwYMCAbMcvXryYw4cP07179+LvrYjkyG5vRWzsd7z88mt4eyczadIM5z6Hwx+HI5DY2BUk9x9DSu9+OHz9AHiFl0jGm9c9p3Pho3lo2E6KU2pqXxwOf+f7P/9swsmTITzzzDv89FNvDh1qTNI5H8I/qM26qT349NH7mTZkOqnpnizY9P/s3Xd4VFX6wPHvuXdqGikk0ksg9N67SC+CihQVxbp2Xcu6rv2nu666lnVX17pWRBTERVREBaUrhN57SUInpGf6Pb8/hiSEJCQhZTLJ+TyPD86dO/eemUwy75z7nvedWvhg4uL7jPl8kJSkyuIpilL5Sv3LdNttt/H1119zww030LVrV5KTkzl27Bj169fn5ptvzt9v3bp1LF68mM8++4yIiAhmzJhRpQNXFKWorVsH88UXITzwwHqiopri84VhGLE4HDfjck0C7KBB1rsfYP51MfZPP6Zl2hmm52zinZ13clNEDk1Q+aRK5XG5JhEW9qf824sXjwBgxIjF+dv0UIOmjVNoFpXCkPYrANiS3IVtKZ3y95GaFVdc0QmcsjpyRODxCFXJQ1GUSlfqzHR0dDSzZ8+mS5curF27lmPHjtGxY0c+/PBDIiMj8/d74IEH+PjjjwkNDeU///kPMTExVTpwRVGKevllCyEhcMcdbUlPX8qZM5tJT1+MyzUdOKd+tBB4ho0k8+NZZHzzAw983A4EvPKKpcRjK8rFsZOV9R5S+t9/S5YMp2XLA7RseSh/D4kdV+srkFrBWpv2jXay/0Qr3N6zdaaFjrPJbRc9ClXJQ1GUqlKma2YtW7Zk5syZ5Obm4vV6iYiIKLLPDTfcQHh4OBMnTiQsLKyYoyiKUpW2btX49lszDz3k4mxvpTJr0kRy880e3n/fzL33umndWs3eKZXH7Z5AZubHhITcz9KlQ7n66q+QEqQMAzRycp7DGXczssF9WI9/hTByaddoFz7DxN4T7enQdB+ZnT/BsDW86DGoYFpRlKpSrgS0C1XouOOOOyo8GEVRLt7LL1uIiJDceefFtWa+/343M2eaefFFK//9b8k1gRXlYrjdY1mzZh/p6WEMGhRGbu4jeL2dcLvHAxYQkN3hDVwNribk0L9o29y/eHaz8x4a9O+HEdKyQuc/eFDDZpM0aKC+KCqKUrlKDaaHDx9+wfs1TSMsLIyoqCi6du3KxIkTadmyYn/0FEUpn02bNBYtMvPooy7Oyb4ql9hYfyD+2mtWtmxx06WLmsFTKteKFf6UjT59JpCbW0z+sxB4Yi4jI+Yy6rcF/gLbHDcyMuTiviCe6+BBfxtxrdTkRkVRlPIpNZg+cuRImQ+2evVq/vvf//Lkk08ybdq0Cg1MUZTiOfGywpLEaZGLDRO9PI146aVLiIqS3H57xYKOu+928+GHFv7+dytffFG0eYaiVMTy5Trt2/vK1DQlLAwaNzbYs6dyot9DhzRatFBfEBVFqXylBtO7du0q9SAul4sTJ06wbt063n77bZ577jnat29Ply5dKmWQiqJApnDxcshqPrNtRUPgQ6IBrt8b4llyCzc/c4rwcFupx7mQiAi47z43f/2rld9+02k9IIvDejoAzXz1iJOhlfBMlLrI6YS1a3VuvNFT+s5nJSQY7NtX8WDaMPzB9GWXqRrTiqJUvkr5ym+1WmnWrBmTJk1i1qxZWK1WZs6cWRmHVhQFOCVyGRb1KR/ZN5OjecjS3ORqHrI1D56nh0JsDp8//CELLBVrtwxw661uohp4mPFSFt2j32NavXlMqzePnjHvMzXiK34zp1T8CSl1TmKijtMpGDLEW+bHJCQY7N2rYVRwQvn4cYHTqWpMK4pSNSo9eywuLo7LLruMjRs3VvahFaVOkkimRM7lqJaNW5w3s7asOSyJh7+sxBXu4L6IRWwxnajQ+d6N+Z3sp34mY/UluBfFk6W5ydLcuISPpZbDXFNvHv+0/16hcyh1z4oVOiaTpH//ss8OJyQY5OYKjh6tWCOhvEoeKs1DUZSqUCVLMZo0acKpU6eq4tCKUuesMCdxWMvAK84LBCTw9GXQMAvuSgT8+dSvhPx20eeabd3K6yFr8Ny2HuLPwBPDwDgnkBHgEF5eD1nD59atF30epe5ZscJE9+4G5amc2qaN/z2/d2/FPqpUWTxFUapSlQTTubm52O320ndUFKVU/7GvI0cUk2e6JB6Wt4DHV4Ddf+lcCvjVcoiTIqfc5/Fi8H9hy3FoXrD44NmlsKkhzO1QZF+H5uXZsOV4UcGJUrrMTNi4UWPw4LKneAC0bl1ZwbTAbJY0bqzK4imKUvmqJJheu3YtjRs3ropDK0qds9F8HM6/yp03K90kA27bUOguq9TZbir/laGfLQfwcM4l+Gu3QqcT8NQw8Bb9U+HBx0+W/eU+j1L3rF6tYxiCIUPKtwAwNlYSGSkrZWa6WTOJqVydFRRFUcqm0oPpN998kz179jB69OjKPrSi1EnFzv6eDIXfmsJ9a8FWeLZPQtHc6jL4xXKQbO2cGXBdwt9+gb0x8HG3Ivtnax5+sRwq93mUumf5chN2u6Rnz/K9L4Xwz05XRjCtUjwURakqpX5Pf/PNN0s9iNfrJTU1lfXr13Pw4EFatmzJ9OnTK2WAilLXxUg72ZxXP/rQ2c4sHYrOQBtILjHKX8IuUxRTo3ribuh1BP7dt8gMOECWcJX7PErds2KFTr9+PqzW8j+2TRsfP/108VPKUvrL4g0YUPaSfIqiKOVRpmBaCIGUpeeamc1mxo0bx1/+8hdCQ1U9WkWpDDMcXXgl5Dd/LnOevGC6eXqR/etJK129l5T7PJcYoWiy8HpDBHDtNnh4NByuB80z8u/SJMQZ5VhNptRJJ04Idu/WmTbt4lrUJyQYfP65RloaREWV//GnTglyclRZPEVRqk6pwfQLL7xwwfuFEPntxDt27IjNVrGmEYqiFDbd2ZmXQ8+r0HE4L5jOKLTZbpi4O7c3okiSdemucrXjU9tmcsV5i8Qm7PYH09+2hXvX5m+2SROTXO3KfR6lblmxQgcod750noSEgkWIffqUPyBWlTwURalqpQbTV111VXWMQ1GUEkRLO69kjeCR8MU48gLdQ5EQ5YCIgjQLm6HTyRfHzc6uF3We7t4GNDLC2aelFb4j4Qy0PQ3ftikUTDcywunubXBR51LqjhUrTERFSTp1urhgNi+Y3rfvYoNp/xdLFUwrilJVqqSah6IolWuqqyOvZo3ELk2EGOazKRf+FA+TFNikiUs9LfgqfTJm9Is+z5tZY7HLYr5jT9gNv7aETH/Sa4g08UbW2Is+j1I3SAnLl+sMHOhFu8hPm2bNJFarZM+ei3tfHzqkoeuSJk1UWTxFUaqGCqYVJUhMdnVge+pdPJtzKdZDMVibZ9PcV4/rnJ1ZknY9MzOvxI65Qufo4W3I7IxJhBkWQoxzguoJe8CjY1mUQJhhZlbGJHp6G1bsCSm13sGDgiNHNAYPvrgUDwBdh/h4g337Lu7j6uBBjSZNJBbLRQ9BURTlglQwrShBJExamOHoin4omhsvaU7imdt4JXskCb6YSjvHAE9TNp25nSdyBtPUF4EuBVr/ZLQoJx3nD2LTmTsY6GlaaedTaq/ly/1fyIYMKV+zlvMlJBjs2VPOjyspMW3dzKHNWcQ3zK3Q+RVFUS5EBdOKEmTS0iA3V9CkSdXlgEZIK39w9mD9mT9w7PRDHE9/kKuG6ST9FEeo9yLqmyl10ooVOo0bG8THVyzFIiHBIClJ4CxjQRB9106i+vcg4vIxHDig0XbtLCKumYTIzCj9wYqiKOWkgmlFCTIpKf5f26ZNqzcHdPRoL6mpGuvWXXxOtlJ3GAasWqUzeLAPUf7iMoW0aWNgGIIDB0r/yBLpaUROHI1+8ADpDisZRJJg7MaycjkR10+r2EAURVGKoYJpRQkySUl5wXT1VicYNsyLyST56ScVTCul275d48wZjcGDK5biAf4uiECZOiHaZs8ClwshJfto7X88+xBuN+bNG9G3ba3weBRFUc6lgmlFCTIpKf5pvuoOpiMioH//inWjU+qO5cv9X7oqsvgwT6tWBkLIMgXT5lUr0BwOAHbTFvAH0wASgXlT0U6eiqIoFaGCaUUJMikpGqGhksjI6j/36NFedu/WOXSogtftlVpv+XITbdr4aNCg4ulIISH+tKayBNNGbCzybB2+xYwghtO0Zbf/Tl3DqBeAXxxFUWo1FUwrSpBJShI0bWpUOA/1Yowa5b9kr2anlQtxu2HNGr1SZqXzlLWih3P6DLDaMBAsYgyj+RGds1dxDIl7xKhKG5OiKAqoYFpRgk5Kilbtiw/ztGghadvWx48/qmBaOY/PifXobOqtG8/uTx8gN1dwWa+9lXb4hASD/fs1jFKym7y9+uCYfgOJ1oGcIo5xLESaTEi7nay33ge7vdLGpCiKAiqYVpSgk5KiVWlZvNKMGuXlt990MjMDNgSlskmJOW01tpQPsR6djXCfLtfDNcdhold1I2zXw1jSVrAssRma8DHOPIKwHff7WyFWUEKCgdMpSE4u/ZJMzvP/YP6E9xAYDG+ThHPqNaT9uBT3uMsrPA5FUZTzqeklRQkiWVmQni4C2hp51Cgfb7wh+PVXE1dcUfFKDUpgmdLXELH1FoTnDEgDhIaQXpwNJpPd/nXQSqkrbniJXDcOzXUccTadYsn24fSKX0eU/TjGsTn4QuJxtHigQuNMSPAfe98+jebNS0kfEYKfD7ahR0+J/sNCsit0ZkVRlAtTM9OKEkSSk/2/ss2aBW5mulcvHzExhkr1qAVMmZuIXH8FujMZzZeDZjjQfDkIw4Xt+NdEbLq21Flly+lFCE9afiCd7Qxlzf6+DO+4BADNyCXk4GtgeCo01rxguix506dPCzZs0Bg+XH3ZUxSl6qlgWlGCSN4l7kCmeeg6DB/uY8kSE14VqwS10N2PIoziW20Lw4E5/TdMGWsueAzr8blovoK53+W7huD1mfODaQCkD1PWpgqNNSZGEhNjsG9f6R9bS5fqSClUMK0oSrVQwbSiBJG87oeBTPMAf4m8tDRBYqJq4BKsNNdxzJmFay6nZkVjGAU5ycKXiz3pvQseR3hzCt3+76+3US8knQFtVp+zk0D4ytgL/ALKWtFj8WIT9esbdO0auC+diqLUHSqYVpQgkpysYbNJ4uICG0wPHerFbJYq1SOIaa7jSGHJv/3b3n40uS+FB2a+nr9NINEdhy54HG+9nsizedWbDnXlf+sm8dDY17BbCoJnYbjwhSZUeMwJCUaptaZ9Pvj1VxPDhvnQ1CecoijVQP2pUZQgkpwsaNxYBqTG9LnCw2HAAJ9qLR7EDEt9hPTnMR842ZIrXvsGt9fCW4vvZs8xf+ArAcPa8ILHcTa5GfC/IZ/739PUC0nn/tH/zr9fouOOGoRhbVDhMSckGJw5o5GaWvIvwMaNGmlpKsVDUZTqo4JpRQkigS6Ld67Ro73s26dz4IDqhhiMDFsTvKFtSMuJZPzL3+P1mVj65FBsZidPzv0bAFIPw9H0lgsfx9qA7NbPsjGpL/9bN4kHx/yTyNAM/+PRkeZ6ZLf/V6WMOW8R4oVmpxcvNqFpkqFDVTCtKEr1UMG0ogSR5GQR0Eoe58rrhqhSPYJXWosXmfyvr9l/ohX/e/AqBrdbyZ/Gv8LcNVNZc2AAvtA2eKIvK/U4zuZ38fTiudQLyeT+ce9i6OFIzYorbgJp/VZi2JtVynjLEkwvWWKiVy8fUVGVckpFUZRSqU9BRQkSublw+rQW8MWHeZo1k7Rv7+Onn0zcdVfFyp4pVcNAssx8mHft69lrOgNAgjeaOx09GexuzsMvDueX7RY+uvsOBnfagPTBQ+P/zduL7+LPc/7DV7fEgih9zmXbNo3vljTlT39yYYxcQrovG8PaGGmuV6nPp0kTid0uS1yEePKkYPNmnccec1XqeRVFUS5EBdOKEiSOHMmr5FEzZqYBRo/y8MabFpxb3iUySuCuP6bSZiGVitmnn2FavXmcEQ5yhCcvrZlkLZM15iOYXriUjM8H8dBDLsb/+XmyT/ZHz96Fpofw8MMuHn2mG0uW5zJ8eCkNUoBXX7UQESG54w53lf78NQ1atSp5EeIvv/hz+EeMUCkeiqJUH5XmoShBIq/GdNOmNWNm2py6lCmxV+LzaayYv4nQPU8Svbqnv9GHL6f0AyhV5qCWzpjIz0nRMsnRCgJpAATkfNWGjCcHYb52O9c8dgo0K64GV5Pb+gkcLR9k+q1RtGhh8NxzVnylxNLbtml8/72Z2293U69yJ6KL1aZNybWmlywxccklBp061ZwvnIqi1H4qmA4mOTmQmlpqRzKldsrrfti0aeADBVP6Wuptmka/5j8RF3GC79aPQzOcCMOFJXUxkRuuUu/TALor4nuyhRtZ3NrQ35rAjKtgYBLeD/7HvRELi+xiscDjj7vYuVPnq68ufAHz3Fnp6tC6tUFysiD3vF4zXi8sXeoviRfoajeKotQtNSKYzs7O5vLLLyclJQWA1atXM2HCBEaNGsU///nP/P127tzJpEmTGD16NE888QTes+3Xjh49yvTp0xkzZgx33XUXOTm1a1ZM37eXeldPoH5CM2jUiOgeHbHOnxfoYSnVLCVFYDJJGjQIfJAatucxhOFA0yTju3/PD5vH4vH6gy5huNCztmI+syzAo6yb9uqp7DCdxhDFvE8ORMEV10KTTJj/BdLuZYv5JAe0tCK7TpzopVs3Hy+9ZMVZQr+V7dv9s9J/+EP1zEqDf2ZaSsH+/YU/vtat08nIECrFQ1GUahfwYHrz5s1ce+21HDp0CACn08njjz/OW2+9xcKFC9m2bRvLlvk/lB955BGefvppfvzxR6SUzJkzB4Bnn32W6667jkWLFtGpUyfeeuutQD2dSqcdSSFyzDDMK5cjvB5wu9GPpBD+x7uxzp4V6OEp1Sg5WaNRI4ke4NLOmvMYpqwt+bcndP+W9NwoVu0ZmL9N+HKwp/w3EMOr87607sBLMbkZ6TYYfx14NVg4C+r7p3a9GMy17Siyu6bBU0+5SEnR+Ogjc7HnevVVC+Hh1TcrDf6ZaSha0WPJEh1dl1x6qQqmFUWpXgEPpufMmcMzzzxDXFwcAFu2bKF58+Y0bdoUk8nEhAkTWLRoEUeOHMHpdNKtWzcAJk2axKJFi/B4PCQmJjJ69OhC22uLkH+/hsjNRZx3yVw4HIQ9+wSlJjQqtUZyslYjUjyE51ShznkjO/+M1ezk68RJBfsAmjMlAKNTkvQMvMXNSt86EfZHw/++gDap+Zu9wuCwllHssQYP9nHZZV5ef91Kxnm7bN+u8d13/lzpyMjKfAYX1qqVgabJYoJpE336+IiIqL6xKIqiQA0Ipp9//nl69eqVf/vkyZPExsbm346Li+PEiRNFtsfGxnLixAnS0tIICwvDZDIV2l5bWBYt9M9IAwsZy228jwOb/06XG33P7gCOTqlOKSmiRiw+lOZYhCyYiQyz5TCxxwJm/3ZtfqqHxN8URKl+NllMjvP2WPi6Azy+Ai49XORu+wUKOz35pIv0dHjjDUuh7YGYlQawWqF588LB9LFjgm3b9DJVHlEURalsNa40nmEYiHNWj0gpEUKUuD3v33Odf7s0MTFhFRt0VbIUXF414eVDbuEYDfkfV2FBEh1XD2LDAzjA2ie2Br6ebjccPw5t22rExhZ/yb36hEN0Dzj9W/6WGwd/wtw1U1m0ZQwTenyHMIVh7fTHYl/Lmvj61haxseGMojXfsYdszqn9/Vp/sHvg3rVFHhOGhRH2VsTai/+5DBsG06fDe+9Z+fMNZ2js2MeWnFZ89104Tz0FCQnV//Ps2BEOHCj4XViwwL996lQrsbHWKjuveu9WLfX6Vh312latGhdMN2jQgFOnTuXfPnXqFHFxcUW2nz59mri4OKKjo8nKysLn86Hrev7+5ZGamo1hBH7GrzghV1xNyDtvItxuRvEz73And/AeNzCTmRGPkBHdCE5lBXqYNZZwp2I7MhPrqW9B+nBHDcbZ9A8l1sKNjQ3nVA18PQ8eFEgZRnS0g1OnAp8Taor/G5FnLkcYDgBGdfZX9fh0xQwu7/UznrDOZGi9irw3a+rrWxvkvbbDaI4RQ8F1x2Nh8FkXuG1Dfp70uaQhuSy1Oaco+efy4M0ZzJkVxzN9f+C9sAf5a/ZHROijuGH0SU6dii3xcVWleXMrP/1k5vjxbHQd5s+30bChziWX5HDOx0SlUu/dqqVe36oTTK+tpomaPcFZgoCneZyva9euHDx4kMOHD+Pz+fjuu+8YMmQIjRs3xmq1sn79egC++eYbhgwZgtlsplevXixc6C/vNH/+fIYMGRLIp1CpHHffhxEdgzT5Z2Bu531e5k/MYRp/aLcMiaoBVRLzmeVEr+xEyIEXMGckYs7cQEjS20Sv7oUt5ZNAD69cUlLyyuLVjC993nq9yeg+F8MSi6GHYTILrh04hwUbJnLcMomMHl+j6pMFRghm7nD0JCQv3ePNPuDR4cHfi+xrN0zcndsL24XmVaSky/1juUu8w0fGjXyVOZqvjEncb7xOy+kj/DXpqllCgg+3W3D4sMDjgWXLTIwY4VVvOUVRAqLGBdNWq5UXX3yR++67j3HjxhEfH8+YMWMAeOWVV3jhhRcYM2YMubm5zJgxA4BnnnmGOXPmMG7cONatW8cDDzwQyKdQqWR0DGmLV+C8djpGWDhYLNzf7zcenrqfT39tydNPW1U532JojmQiNk1D8+WgnZ09BRDSjTCchO3+M+YzKwM4wvLJa9hSk7ofeqKHkDpkD5ldPiGn9f8x+fpI3F4rn+3/EPTQQA+vTns0dwCjXK2wZ9rh7d5w1U5ofabQPiHSxDh3An9y9L/gscwrl6MdOsiTxnOEkc21zCacTB6Ur6GlnsLy4w9V+VSKlZDg/z3Yt08jMVEnK0uofGlFUQKmxqR5/PLLL/n/379/fxbkJcGdo127dnz11VdFtjdu3JiZM2dW6fgCScbFkf3qv8l+9d/ExoaTcSqLP0vIiHDz7rv+hgmPPFK9i4BqOnvy2wjDU+L9wnAQcuAFMqK/r8ZRXbzkZA0hJI0a1bBvTkLHU38knvojadMc2rf3MWeOmZtuKvm1V6qehuDdrPHc/vEpvkmzY3toLZrhv7olgChp58Hcvlzv7Iwo5eqWOXENIjeX+uTwKC/xBH/nj/yLaNIgG8yrVuAeP6EanlWBvGB6zx6NM2cEZrNkyJDApz8pilI31ZhgWikfIeBvf3ORnS14+WUrEXoODxqvYv32G6TJhGvatTim3wihdXOG0Hr860IVJzYc7M7prPqM6vJz/jZz2iowvKDV/F+DlBSNhg0lFkvp+waKEDBliofnnrNx4IAgPr6GBf51jM8r2PhmS3r39vJMh34cyE4HoJUvil7ehqUG0XlkWBhYrOBy8hCvEUIut+GvIS5NJmR11sU7KzISYmMN9u7V2bRJo18/H2HBl2apKEotUePSPJSy0zR47TUnE4Zn8tSL0Xzx2mlMO7dj3rqZ0OefJWrEYERGeqCHGRDCcBW6/dCs17j1/Q/O3wtkcMygJieLGpXiUZLJk71ommTOnEBXHFEWLjSRlKRx990e+ngbc42rI9e4OtLb26jMgTSAa+JV+f9vw8UD/IswznaZNZlwTZ5a2UMvlWndWtr7trF6zgl27tQZ1XCLal+vKErAqGA6yJlM8Jm8jtH8yO3et5jLZMDf1EVPTiLklZcCPMLA8IZ1yP9/j9fE2v19SDnTlCxHwfSVYYkBzVZtY8oQTt6zbeD28O+4KeIbng1Zxj79TOkPxD8z3aRJzQ8WGjSQDBni46uvzBg1P/avtaSEt96y0LKlwZgxFUt/MBo0JOfxp5H2EM59B8qQEHLvug9ffOuKDbacrLNnEXn1BNqfWc1hn7+W+ZULbifskdqzVkZRlOCigulgl5tL+Iqf+JqrGMBqpjOLHxkFgHC7sX1ZN1uOO5rfj3F2EdzmpK443CEA7DneBgCp2XA0u7taKk548PFo2GI6x7zD86ErmG/bzULrPt4NWc+wqJmMjfycZC2zxMd7vXD0qKBZs+CITqdN85CUpLFmTYD7ntdha9bobNigc+ed7kppP++4614yZn+Fe/hIfM1b4B4ylIwPPyP3sacqfvByEJkZhD/6EMLhoAP+FujNOURH53psc7/EtD6xWsejKIoCKpgOeiI3F4RGCA6+43IS2MsDvJ4/gyRycgI6vkBx1x+Fu/5opBbC6j0D8rfvOtoOQ7PiDW2Lo9ldVT4OLwbX1vuaL2zbcQofDq1gltArJE7hZZPpOMOjZpbY0vn4cYHXK4JiZhpg7FgvoaGSOXNqfi56bfXWW2aiow2mTau8NCbPgEFkzp7HmcQtZHy1AM+wEZV27LKy/PgD8uy3g3bsAmAsP/iTVlxObLM/q/YxKYqiqGA6yMnoaIzIegDUI5OHeZVdtGc1/gDS27lLIIcXOEKQ1flDclo9wcp9w2gUdRRd87LzWBecTf5Aeu8fQbdX+TD+ZV9DoukoDlHypXafkGQKF9PrfY2kaMCcV2M6GHKmAUJCYOJEL998Yya3aI8QpYrt2yf48UcTN9/sISQk0KOpXCI7G+Hz/y71Yh3t2MkMPvXfZxiI9LRADk9RlDpKBdPBTtPIfeRxjLOfmlOZQxhZfMCtSLudnL9U72XYGkVoOFrcx6rDE+k3yEaL5h62eB4gp+3fQa/6KMODj3dD1heajS6JISQpehbrTceK3JdXYzpY0jwApk71kJ0tWLRIzU5Xt3fesWCxwC23BMfi2vLw9O6bn5oVTRo76UB//M1ojNBQPEOHB3K7u88HAAAgAElEQVR4iqLUUSqYrgWcM24m9+FHMUJCCAnXmWaax5dM48jz7+AZOizQwwuoI0cER49q9OofQkJbE/v2V19w96vlEF7KHgA78PC+fUOR7cnJ/l/Txo2DI80DoH9/H02aGKqqR5XzYbH8QFjYg8BNZGe/y5w5JqZO9RAbGzzvl7LydeqMp0cvpNVaaLvUNAgJxXnV5ACNTFGUukwF07WBEDjue5DUHQfI/HQ2U//enlxCmSOrv2RVTZOY6M+v7NPHR+vWBvv3a/iqqVHaQT0dtzgvmPYJ+MdAeLRovqkUsMdUtLpHSoogNtbAXvVZKZVG0/w1p5cu1TlxQvV4rgpm83JiYhIID78Vu/0D4BM+/tiJ06nx8MNPArVvZhogY+aXuEeORlqtGBERSJsNb5dupP2wpM7W1VcUJbBUMF2bhITgGTiYbjd2oF07H7NmqVnBtWt1QkIkHToYJCQYuN2CpKTqCe6K1PI9XA+G3gSPjoR/DIL9UcU8pqjkZI2mTYNvlnHKFA+GIZg3T6V6VDazeRX16k1B006jadkA5ObaeeutO5g48Ru6dHmTiIgboJgc/KAXFkbmh5+Run47GZ/N5cyy30n/aSlGs+aBHpmiKHWUCqZrISHguus8bNigs2NHcP+I9Zy9mM8sR8/ecVFNGRITdXr08GEyQevW/lniffuq5zVp7Y3CIs+e6/PO0OUu2NwAXv7Jv21ux0L7a1LQ3hNb5DjJyVrQLD48V+vWkp49/e3FN5tOsNx8mLWk4KaaLg3UWpLw8NsRwlFo66efzuD06Vj+9KdXEMKB2bwMs3lJgMZY9WRcHN5+/TFaxgd6KIqi1HHBHWkpJZoyxYvZLPn88+CcnTanrSJqdT+ifh9ExObriFozjKhV3bCcWlTmY+TkwLZtGr17+4O3vGB6794qeNtLieXb+dQbP5LoHh2JuOEahq8+gTkjBK6fBNOvhk4nYfPb8KfV0C8ZviwcTFvRucPZo9A2w/DnfQfjzHSGcBI9fQ87duhMPLSUWyK+ZQSf0iHmLf4aspzTonylPjz4OKplkaxl4qilKQxlYTavRojC6UA+n8Zrrz1Enz5rGDRoJQBC5BAS8noghqgoilKnqOuvtVRMjGTsWC9z55p56ikX563XqdHMqb9Sb9M1CMM/85b3r8lxkIgtN5LV4U1cDaeUepxNm3R8PpEfTEdFQf36RuXPTEtJ2IP3Yp0/D+1sLTjtSAo7f3Uio7+GU+Hw3C/w2EownZ1hnrYdHhwDe2KgTSomqZHgjaaL95JChz51SuByBUcr8XMd1bK4PHI2J2cY8GgbHDM74uj+o/9ODd4N2cBs+3a+TZ9GK1/0BY91SEvnffsGPrdtwxASIf3lBC93teHu3F509sVVwzOqOfzBdOFZ6VmzprN3bxvmzJmS34dICDCbVRMTRVGUqqZmpmux6dM9pKUJfvghiL4zSYPwbbfnB9DnE4aDsJ33g6/4+8+1dq1/8WGvXgVpBQkJRqXPTJvWrcV2TiDtwcQz8v8Y6v6J6BOpDPjpB0KeXF0QSANM2e7/98uOmKQg2rDzWeZVRY6dVxavadPgCaadeLki8kuOadm4Y3Jgwm6Y1Rm8Ba+7W/hIFblMjPySDOEs8Vg/WPZxafQnfGzfTI7mwSG85GpeXMLHfOsuxkfN5m37uup4WgEkKbyY0ANnq8R4vTpPPfUcN930MT16rOeqq/533mOD532jKIoSrFQwXYtdeqm/PFkwLUQ0n1mO8JXWtVFgPbmg1GMlJuq0besjMrJgW+vWFzkzbbiwnJiP/dC/sB35FOFOzb/LNvszcPiD+/X0YDAr+CtPM4NP2RAykO93H+WO3F6EGmZCjbM/i8ZZiEFJiDmdGOpuwS9pN9DACCty2ryGLcGU5vGNdTenRS4+cXbMMzbDyTD4qVWh/aSAbNzMsm0r9jirzcncEfE9DuHFc35VFPyz007h5cXQVcy2bq305xFoJtMGwsNvpH79+mf/iyMs7C6kDEPKMI4ebciIEYv529+e4qabPmb58iGYTIXz0X2+ZgEavaIoSt2hgulaTNPg2ms9LFtmqrYKFhWl5+5HyIImJzuPtKPebems2l3QElz4stFz9l3wOIYB69bp+SkeeRISDFJTNVJTy/56WI/OJmZpPOHb7yZ033OE7v4zMSvaEbrrEZA+REY6a2QfxvMdvVjPXhL4kql8xC2EGxno2Tk8ljuQHal38VL2CG7N7cYNjs6MvtKB3BbHM4mTiZPFl/RKSsoLpoNnhvGNkLXkaOfMpI7dBzG58GnXIvs6NC9v2ddhFFN14tGwJTgv0Dky/xjCy5NhS2vVwka7/Q0iI8ditX6DEB6EkAjhxGb7ktDQv/Hzz5fSrdsmEhN788knM/jww1sJDS2cg24YoTgc9wfoGSiKotQdKpiu5a691v9BPHt2cMxOS1MoUuj5tz9YeiuZjnq8s+TOgp2EBWkKv+Bx9u3TSE8XxQbTefeXhfX4PMJ3PoDmy0LzZSOkB82XizBc2I98yuYv/sO43f+mP7+zhr78ncc4SEumMtc/VMDTtz8AdsxMdXXghZzhvJo9ipdHN0bTJPPnl5yGk5IiiIyUhBWdtK6RnHjZr5/X0tnig2u3wvx2kG4r8phMzcWJs+Xd8mwxnSBJzyjzeQ0kC617L2rMNY3FspDQ0L8hhANx3oy8zyd5+unHGTt2AXFxp0hM7M2MGTOLHENKHSmjcTpLX1ugKIqiVIwKpmu5Jk0kQ4f6mD3bXG3NSirCXX8UQvoH6vXpfLbqegC+XjeJbOfZ2Vuh4YqbcMHj5OVL9+lT+EmXqzyeNAjb/eci+dtSwi/bL2PYc98z8o9PsC21IS+FPctBEc9jvEgEWf79rFY8vfvia9+h2MNfcolkwAAf33xjKrHqX0qKFlSz0g7hQS/uz8pNm8Blgnd7FrlLlwLHeTPQSywHi59pPmOHI0W/SOVoHr6x7r7ocdckoaHPFVlgCJyX1vERq1dfTfv2B5Gy8JcxwwjDMJqQnv4jEFJNo1YURam7VDBdB0yf7uHoUY1ly/TSdw4waY7G2eh6DM3Oz1tHciKjAQ+Pe4VcVyj/S7wKQ7PhjhmOEdLygsdJTNSJjjaIjy8cpTZpIrHZZJkWIZrTfyuy0PGX7Zcx+LkVDP/7L+w+1pZ/3vAw2796nluXTcbWvS3SZvN3ZbNacY0dT8Ynsy94jokTvezbV3I98OTk4KrkES6txaZs0PMYjNsDLw2CjMKlZTzCR6RReMY6XTgLcq7zGALGTofRNxR77nThqtDYawJd34OuHyyy/aefRp6X1nEb4eGHSE9fgNN5PT5fYyAOj6cn2dlvcObMegyjSfU/AUVRlDpIBdN1wOjRXmJiDD77LDhSPbLb/gN37Dg+WXkL0WGpPD/1CVrGHuDTlTfjjexHZucPSj1GYqJG795GfpmwPLoO8fFlW4SouU4Uur3/RDwjX/iZw6eb8+aN93Dgn/E8MOY1wtiP0bQZ6Yt+4cyKtWTMnkfqxp1kvfdxqe2NL7/ci65LvvmmaKqHlMHX/dCExmXuFpwfBwPwt18gzQ6v9S+0uZM3jmhZuFd6lLRjkuf98GZ3grVNYHscnCj6ukYbRVNIgo2mpSBlwe+plPDii48yZswi4uJOsm5dr/y0DiktSBlKdva/OXNmJ3CC9PRfcbmuBiyBeQKKoih1kAqm6wCr1d/E5ccfTZw6FQQLETUTKc0/Yv76q5kycjM0GMK0MVtYsv0ydjVcAPqFL12npgr27Su6+DBPWcvjGdbCNZ+X7bwUQ+r8+Oho7hn1FjaLCyksGLamBY9p3gJv777I+vXL8EShfn3JoEE+5s83F0n1SEuD3FwRVGkeAPc6emOXxXxx634cJm/3B9On/D/DEGnm3tw+RXYd6YrHzDlXUhwmeGwExJ6t9LKycJWKMMPMla52lfYcAkVKO3ktwF0uCzfd9DGPPfYi11zzBWvX9qF9+135+wrhA+zFH0hRFEWpNiqYriOmT/fg8Qjmzg2OmtMLFphxuTUm3dmXzB5fc+U9I5BSMO/r0mfX16/3v61LCqZbtzY4fFjgKiUrwBPZD/SCYGXlnkFEh6XSrlFBQIPQcDa6rvQndAFXXunl0CGNLVsK/zomJ/tvN2kSPDPTAP08jRnjboXdKOa99tyvkGuGlwZhkzp93Y0Z625VZLeOvljivVEFG17vB8n1YNY8sHuKBNM6GmOKOU6w8Xq7A5LTp2MYMWIxn356I8899xSzZk0nJKRwypFhhOPzqVbaiqIogaaC6TqibVuDXr18fD5TR1+7Bn1/za58MGeOiYQEH926+Wdl4+MlPXv6mDu39GB67Vodk0nSrVvJM9OGITh4sJS3v9DJbvMSUvMH1Kv2DGRgm1Vomj+4NTQ7zgZTMezNy/HMiho3zoPJVDTVIy+YDraZaYHgzayxTHS1wSZN6Oema7Q/DTdsgTf70PdgOz7JvKL4BYvAy9kjsEsTnAyFFwbDFbtg5AHomwIrCl5zuzTxj+wRhWeyazrpw3JiPpFrhhGztCXRKzoRsv/vCFcmGzf+mT59Elm3rhdffjmVp576W5F0JcOw43Dch/oTriiKEnjqL3Fd4XZzi/lT9uw3s2PqS0QNG0TU4D7oO7YHemRFHDwoWLPGxLRp3kJBxJQpHnbu1Nm27cJv28REnS5dDOwlXAHPK49XllQPV8PJZLX/JydyWrDnWFsGtPkNQw9FalacjWeQ3eFfZX5eJYmK8jfYWbCgcKpHSkrwdT/MY0Ljjeyx/JB2HZOd7YkwrJikRgRWRvzlMCZDp8VzV2Cj5CslvbyN+DjjCkxPD/OnefzjZ/8dg5JgYwP0TBs2aeKv2UO5KphSPAwX9dZPJHz7XZgz16F5UtGdSYQc/CeJbz3CZUP/gsMRytKlI5k6dW7Rhxt2fL4uOBx3FnNwRVEUpbqpYLqOCHvwPq7f+GfCyOKj3GsQDgf6nt1EThyNOHky0MMrZO5cM0JIJk/2FNp+5ZUezGbJnDklz057PLBxY8n50uBfgAhlC6YBXI2uY5FpEwA9hrUjp82LpA7eRU67l0FUzmzoFVd4SErS2LChYEzJyRqhobJQB8dg09EXyxvZY9mXei9HTz9IBo/xecwwbrjew6xZZg4dunAOf6Nt8Rjv96DbbcnUa52NRWqYB6WAoTF8+SB+TbuBGc6izWBqstA9T2HOWId2XqfP//x4G5e/OJf4mF0sWuika9cGSGnFMEKR0nL2Xxsu1zTS079FLTJUFEWpGVQwXQdox49hW/A14c7TTONLvmQaWYQhpES4XNg/+m+gh5jPMGDOHDODB/to1KhwrnB0NIwY4eXrr014S2iMt22bhtNZtFnLuUJDoUmTsi1CzLN2nR2LRdJ+zCScTW5EWmLK/NiyGDfOi8UimT+/4ItCcrKgWbOiFUlqg4cecmM2w8svWy+437PPWgkLg9l/rM/u1HvYnnoXv7UdhaZJOv3am1a+6GoacSXx5WA/8kmh2uVen849H73JfZ+8yeXdv2P5syNpqS8mK+sjUlN3kZ39Mjk5/0d29j9JTd1Ldva/geCvXKIoilJbqGC6DjBt3IC0+GexbuO/5BDGV0wGQLhcWJYuCeDoClu7VicpSWPqVE+x90+Z4uXkSY3ly4ufES6pWcv5WrcuW3m8PGvW6HTtamCrohgmIgKGDfPy7bcmjLNZHcnJWtAtPiyrSy6R3HKLh6++MrFrV/E/h2XLdBYvNvHggy5iYiQagnrSRrNQG506GaxZE0Q50meZMzYiRUFqi2EIrnjtG95afA+PjP8HXz84iQjzCSynvgNAyhhcrutxOO7F5boGKesFauiKoihKCVQwXQfIc3pR92UNMZxmNQMK7o+ICMSwivXllyZCQiTjxxc/9TxypJfISFniQsTERJ2mTQ0aNLhwEJpXHq+kzoPncjhgyxaNvn1LmA6vJBMnejl6VCMx0R8kpqRoQdWwpbzuu89FaCi89FLRdAWfD555xkqzZga33Vb0i1W/fj42bNBxu6tjpJXJh7/JvN/Oo+1ZuGk8f538JP+47lF0zf/zFkbxXyYVRVGUmkcF03WAp98A0PwBmgB6sIH1+Ns6y5BQHDfcHMDRFXA44JtvzEyY4C2x14nVChMnevjhBxPZ2YXvk9I/M32hFI88rVsb5OYKjh0rPYdi82Ydj0eUOttdUaPHeLDYDO5bmMRgbTYZGYKNrXeyTa9ZOe2VJToa7rrLzfffm9m0qfCfoi++MLNjh85TT7mwFpMJ0revD4dDFCknWNN5wzohZEFNxjX7+gIwpW/BQkNDD8EdPaTax6YoiqJcnOD6JFIujtlM1lvvIe12pKbRgw1soxMOeyTuAQNxjx0f6BECnA2QRYkpHnmmTvWQmyv47rvClSCOHBEcP66VKZguT0WPvHSC3r2rbpZ4v36GEc0+xDd2D4f+15jdyf5zbW61h/FRsxkX+TmnRG6VnT9Q7rzTTXS0wQsvFETM2dnwwgsWevXyMXFi8VcD+vb1/4yDLdVDWmJw1R+DFP4rK2v396FeSDoJDQpKVQrpX/SqKIqiBAcVTNcR7pFjSFu4BNcVk+gWk4QHC2vveZ/MmV/6e2zXAHPmmGnc2GDgwAsHw717G7RoYRRJ9cjLly5PMF2WvOm1a3USEnzExFRN/vIBLY3RkZ9zSEvHd81WOB4On3UBQLZIxyG8bDKdYFTUZ6QJRylHCy7h4XDfPU5+/dXEtl73EN05gf+OXMDJkxrPPusscfFlXJwkPj4486az27+OYW2EFBbWHuhD7/hENE0iEUjNTmaXj5CmmpN6pSiKolyYCqbrEF/HTmS9+yHxC/8BwPqGl9eYQPr4ccHSpTpTpnjQSuulImDyZA8rV+ocPVoQbSUm6oSESDp0KH0GOS5OEhEhS52ZNgx/MF2VKR631FtAtnAjBTB+D4S44T1/Gg4t0gHwCoOTWg6PhC2usnEEhM/Hg0uvpqE4xtNJf+D4CZ1/7r+KqfpXDDz+9QUf2revjzVrChZsBgtpiSGt3wpSGzzElqQu9G61Dil03PXHkN57Ee7YsYEeoqIoilIOKpiug1q08AeSmzfXnB//vHkmDKP0FI88kyd7/O3F5xXMTicm6vTs6cNUho7pQhQsQryQPXs0MjJEflpBZdtiOsFBPR1DnJ31DvXA5Xsgywo2D8QV1CL2CIMfrftJrUXpHpYfvid8/Qqeks+xksGM53t86Lzoe4TwB+7hQj3f+/XzkpYmylXisKaQ5kjW5D6JzzDR/qo7OT38NJndv8Qb0T3QQ1MURVHKKfg+hZQKEwK6dPGxdWvNmJWW0p/i0bOnj9aty5ZKER8v6d3bx9y5JqT059lu3162fOk8ZSmPl5dGUFUz0zNtW3Bz3rGnne1K2Szj3MIPAAgpmG/dXSVjCQTbrE/QcnO4lQ9owUE20437+TctOQSAedWKEh+b9wXn999rxvu4vDZu9I+7e09zpTX/URRFUaqfCqbrqM6dDXbs0PDUgApc27Zp7Nypl3lWOs+UKR527dLZvu4Em1cl4/NduFnL+RISDI4d04pUBTnX2rU69esbtGxZNfnSB/V0fOK8Y4/dC2EuaJ5RZH+n5iVFz6ySsQSClpUFgAUPb3AfQ1jGEzzvv1OCyMkp8bEtW0piY4Mzbxpgwwadxo0NLrmkdtYSVxRFqStUMF1Hde3qw+US7N4doLeAx4Pl228IfewR5j2yEYvZ4MoryxlMD/wei8nNgv8sZOu38wC4zHormuNwmR7funXpixDz8qWrqguhWRYTCNq98Ml8eHpZ0fskWIp7TJByjRiFcbYTzuV8zzKGEon/S4TwuPH26VviY4Xw15sO5mC6e/eqLbeoKIqiVD0VTNdRXbr4P8QDUadXO36M6P49CP/jXZg/+IC5G9owwfcNjd/5a5mPYUt6n+ZJNzC+23fMXjWVFTv60rHJNuIcc4j6fQha7sFSj1FaebwTJwSHD2tVuviwj6cRtuKC40k7YVBSkc1h0kxn7yVVNp7q5pxxM9hDkOd9WzHsdpyTpmBc0uCCj+/b10dyssaRI8HVcz011f/e6t49yFZPKoqiKEWoYLqOio+XhIZKtmyp/lm98NtuRDuSgpadzY+M5hRxzDA+IuTd/2D+tfTW5sJ1krC9jyMMBzMGf8qJjAb8vG0UAxJWIzAQ3gzCd9xX6nFatDAwmWSJM9N5pfaqavEhwPXOLpTnIr+Oxmh3fJWNp7rJ6BjSf1iMt1sPpNWGER6OtNlxXn8j2a/+u9TH9+sXnPWm85rU9OihZqYVRVGCnQqm6yhNg86dfdUeTGtJhzFv2YTw+YOIT7iR+pxiDIsQubmEvFV6AGU78gl5K/PGdVtIdFgqAAMSVgMgMDBnrEFzplzwOGazP6AuaWZ6zRodm03SuXPVzR7GyhCucLbFJksvQRJimLg/tw9mgitwLI0vvjXpP/7Kmd/WkzF/Iad37Cfn+X9QlrIsHToYhIbKoFuEuGGDjqZJunZVwbSiKEqwU8F0Hda1q8H27Rre4pvMVQn9SArSYgEgFzvfMoFr+AIL/nxp/fChUo9hztyAMJwAWEwepvX7EoABbVbn7yM1K3rOnlKPdaGKHmvX6vTo4ePscKvMq9kj6eqNu2BAHWKYGO9O4F5H76odTAAZTZri7dwVwsLK/BiTyd+kJ9hmpjdu1Gnb1ijPU1UURVFqKBVM12GdO/twOESZugBWFl/zFoiztYOXMwQXNi7nOwCkEHgT2pZ6DKkXjkCevuo5PvjDLYVaMiMlUrOXeqyEBIMDB4p+ocjJga1bqzZfOo8VE/PSp3J3bi8iDCthhgWL1LFKnTDDTJwvlKdzhvBm1ljE+bXyFPr187Fzp056eqBHUjZSwoYNmlp8qCiKUkuUob2FUlt17epPX9i8WaNdu+pZCGU0aoy7/0Asq1bys2ckFlwM5mwtYZsdx71/LPUYrgZXYzn1PZrPX9OuQeQJbhn6UeGdhMBbr2epx0pIMHC7BUlJgvj4guzlDRt0fD5RLcE0gAWdv+QO5OHcfiyxHOSwnoEuBW18MQzyNENTQXSJ8nLa167VGTWq5geohw8LzpxRiw8VRVFqCzUzXYe1bm1gt8tqb96S9c4H+Nq04WcxmkGsxGaTSKuV7MefxtN/YKmPd9cfibTUR5aQO2xoIeQ2/yNopednlFQeb+1aHSEkvXpVb3BmRmeMuzV3OHpym7MHQzzNVSBdiu7dfZjNMmhSPfKatajFh4qiKLWDCqbrMF2Hjh2Nam8rLqNj2PXFarbKTlw61CDn6edIXbcN5x13l+0AQie95/cYtkYY56R8SHSkFoKrwdU4Wj5cpkPlBdPnL0Jcs0anXTuDyMiyDUkJnJAQ6NLF4Pffg+NC24YN/oWt1XU1SFEURalawfHpo1SZrl19zJ5txjD8FT6qy/Ll/rfegCcG4Ozar9yPN+xNOTNwI9aTC7Ad+RThzcAb2h5nszvxRnQr83EiIyE2tvAiRJ8P1q3TufrqGtAeUimTfv18vPeeGYcD7KWnygfUxo0anTsbmM2BHomiKIpSGdTMdB3XpYuP3FzB/v3V+1ZYtsxEdLRRsbJzmgVXg8lk9FxAet9lZHd6p1yBdJ6EhMLl8Xbs0MjOrr58aaXi+vXz4vEINm2q2akeHg9s3aqrFA9FUZRaRAXTdVyXLv5gtjo7IUoJy5bpDB7sq9bZ8JKcXx4vr1mLCqaDR+/e/p9VTa83vWuXhsMhVDCtKIpSi9SAUEYJpDZtDKxWyebN1ReE7NqlceKExtChNSOgSEgwOHNGIzXVv9AvMVGnQQODZs3K05tQCaToaGjXrubXm96wwT8+VRZPURSl9lDBdB1nNvsXIW7dWn1vhWXL/AHFpZdWY7eYC0hIKLwIcc0anT59fAhVRCOo9O3rIzFRx1eD49SNGzWiow2aN1df1BRFUWoLFUwr+W3FjWoqLrBsmYlWrQyaNKkZAcW55fGSkuDIES2/drESPPr29ZGVJdi9ew+6vhVwBXpIRWzcqNO9u6G+qCmKotQiKphW6NrVICtLcOhQ1X/Cu1zw2296jZmVBmjSRGKzSfbs0Vi1yr9N5UsHF13fyogRdwKwefMXREaOISamJaGhjyNEWoBH55ed7U9xUikeiqIotYsKphW6dPF/uFdH85Z163RycwWXXlpzAgpNg1at/IsQV62CkBBJx46qBnCwMJt/ISpqBK1bz6Rp0yRWruyNpmWhadnY7e8RFTUIIU4Fephs2aIjpVp8qCiKUtuoYFqhXTsDs1lWS/OWZct0dF0ycGDNmZmGgvJ4q1ZBz54+TKoCe1AQ4jQREdMRwoEQksGDV7BixWCkzLvfjaYdJyLi+sAOFNiwwf/7pdqIK4qi1C4qmFawWKB9e4MtW6p+ZnrZMhM9ehhERFT5qcpMnD5N+5PLSToMWzb5GBC22V8QWKnxbLaPgYLgdPDgFRw/3pADB+LztwnhwWzeiK7vqf4BnmPDBp3mzQ1iYmrGWgFFURSlcqhgWgH8qR7+y9BVd460NNi0SWPo0JozK60f2Ef0gJ50TvwEiYaBzmW//B+Rk8b7E7yVGs1u/xRNc+TfHjRoJQArVgw+b08vVuvcahxZURs3qmYtiqIotZEKphXA37wlPV2QnFx1ixBXrjQhpahRiw/D77kDkZFOe88WADR8DHAtRd+yGftH/w3w6JTSCJFe6HaHDjuIjExj9eoB5+3nRdOOVefQCjlxQnDkiFp8qCiKUhupYFoBChYhVmXzlqVLdcLDJT161IycUe3EcUzbtiCkpA17EBh0ZTPhZKM5HNg+ej/QQ1RKIWXhfCFNk3Ttuplt2zqdt5+OYcRV59AK2bhR5UsriqLUViqYVgDo0MFA12WVNW/xtxA3MXCgt8Ys7hPp6UizGYAQHAxhOVczL/9+LSMjUENTysjpnI6UtkLbOnXaxrZtnRVtgEMAAB7cSURBVM5LWbLgck2u1rGda+NG/8Lbzp3VzLSiKEptU2OD6ffee4/Ro0czYcIE3n77bQC2b9/O1VdfzcSJE7njjjvIzMwEIDMzk9tvv52xY8cyffp0Tp0KfBmsYGOzQdu2VbcI8eBBQVKSVqNK4vmaNefcTjVLuYwn+DsAEvB06xGgkSll5XDcwvl/xjp23E5WVgTJyU0BkNKE19sBn69DAEbot2GDTocOBiEhARuCoiiKUkVqZDC9evVqvv32W+bNm8f8+fPZvHkzP/30E88//zz3338/CxYsoGXLlnzwwQcAvP766/Tq1YsffviBKVOm8Pzzzwf4GQSnrl0NtmzRqmQR4rJl/unomrT4ELsdx213Iu3FRDh2O7l/erT6x6SUi5SXkJn5EVLa87d16rQNgO3bOyKlGcOoT2bm54EaIoaR1/mw5nyRVBRFUSpPjQymd+zYwaBBgwgLC0PXdQYPHszixYsxDIOcnBwAHA4HNpv/8u7SpUuZMGECAJdffjnLly/Ho0qblVuXLj5On9Y4dqzyFyEuW6bTpIlBfHzNKguW+9hTOK67Hmm1YoRHQEQERkQEmW++i7dXn0APTykDt3ss6enf4XYPRkor7dsnA7B1a3eczumkpa3GMBoGbHwHDggyM1WzFkVRlNqqRgbTHTv+f3t3Hh9Vdf9//HVnJpkkJBCCCUEMKlvCXsUiEb6mQQhChIRoAf0pKrbVikL54kKRgu2vBRcsDxWtrQu0FVm0ioKAbD9RQiQFy6ICogjIahYgmayznN8fqYEYUJxMmCS8n//g3Ln35nPPY7zzzsm553Rjw4YNnDhxgoqKCtatW0d+fj6TJ09m6tSp9O/fn40bNzJ69GgAvvnmG2JjYwFwOBxERkZSWFgYzEtolOrrIUSPp2omj5QUD1b9r1j+49jtlMycRcHWXRT/5UV46y0Kdn5F5bDMYFcmP4LH81NOnnyXwsJtOBwv0KZNOVu2TMXlegZjLgpqbR9/XPX/kx4+FBFpmhrIo2A1JScnk5WVxW233UZ0dDTJycls2rSJRx55hHnz5tGzZ0/mzp3Lww8/zN/+9rdaxxtjsNnO/feEVq0iA1l+vYuNjaqX8/7sZ1VLa3/5ZTj//d0kID76CIqKYPjwUGJjQwN34kCKjYKky6r+M7iVNHn19fmtkggk0qMHfPEFAf0cf8tg2MjXbOMobnwk0JyhdCKMkO/sWQR8xa5dbWnWLIx+/Zphr+d1keq3bUXtW7/UvvVHbVu/GmSYdrlcpKWlceeddwLw0ksv4XQ6cTqd9OzZE4BRo0bx9NNPAxAXF0d+fj7x8fF4PB5KSkqIjo4+559XUODC52tYww/OJjY2iry84no7f+fOEeTkGPLyyn5453O0ZEkolhVKr14uGvqzofXdvhe689W+7ds7+eCDEI4edQU0wL7u/IzHm2WTb5XhswwGQ4ixYzDcXt6Lh0uuIdK+n4iIGTidyzAmhNzctVx11U4qKuZTWvpwvfWU67Nbv9S+9UvtW38aU9vabFaj6+CEBjrM4+DBg9x77714PB6Ki4t54403uPvuuzl69Ch79+4FYO3atfTo0QOAlJQUlixZAsDy5cu56qqrCAn5bi+RnIsePaoeQgyk9evt9OzpIyYmoKcVOaukJC/l5Rb79wduXNHvm63ngajVHLAXUWpzU255qLC8uGyVlNjcvBz+Hx5q+RjR0dfidL6FZZXjdlewdWtP+vTJITz8FVq2TMZmOxiwmkREJPgaZM90UlISaWlpDB8+HK/Xyx133EHv3r2ZOXMmv/nNbzDG0KpVK2bMqJrGbMKECUyePJn09HSioqKYNWtWkK+g8erZ08vrr4dw7JhF69b+9dYftBXxj7DtfObIw1PsIHfzaMaMKwlwpSJnl5RUNT551y477dvXfQaZRc5PeSV8K2XW2c9ls8p53v4sNqu8etv27T2prHRy9dWbsCw3Nls+LVrcyPHjHwEN7QECERHxR4MM0wDjxo1j3LhxNbalpKSQkpJSa9/o6GheeOGF81Vak3Zlh53AFXzx5kw6pBylLGEs3qie53TsCauce6OW82HoAQxQaXlhdWfw2Jg/bAn7m3t5oXgoLU+bxkykPnTu/G2YtjF0aN3OZTA81iz7e4M0wM3sIMSq+ZDhpk1XA9CnTy4AluXFbt+Pw/FvPB7NFiMi0hQ0yGEeEgTeMpp/PIL+5ddjWVWLt4Qd+jstc9Novu1W8H3/VIMnrXLSol/lg9ADVFjeqiANsLo9hLtx99/HhtADDGz5KsetwI3HFjmTyEho187Hrl11v8V9FHKIE6f1Np/N3WwhikqMgZKSCA4cSGDdugHExx/hkktOH9pRRljYq3WuS0REGoYG2zMt51fUJ3cTcjwbp7OczvGf8/G+K7Hwgq+U0PzVRO76X1xdnz3r8ROjVnHYXkzld3rmWNUBrt0PTi9u4JithPFR7/HPIk09J/WrS5fAhOkdjm9wf/dzDZDbFv7WGwrCoTCcWwtG4SpsQUFBKyorndW7ZWX9q8aUkJZlsNmO1LkuERFpGBSmBVvZAZz5K7B8FQBcednHbPi8f/X7lq+MsCMLKen4KCa0Va3jv7FKWB26t3aQPtgcdsXCLz+u3lRpeXk/dB9HbS7ifY3viV1pPJKSvKxdG0plJYTWYUZGN158nOH5gakD4MN20KkQYsq4JHEvHWIO06pVATExhdX/9u+/ocZhxlj4fHH+FyQiIg2KwrTgzFvG6Vmh9+VbWJBzC3lFFxHbPB8AYzkIzVtBRdtbax2/OOzTM594dfuqfwd9Weut15w7+N+y5DrXLnI2SUk+PB6LL7+00aWL/wumXOyLIsw4cFmVpzaWhMD6S2Hcv+HP71X9PHJ5gjVE8v1DooyJoKJitN/1iIhIw6Ix04LlKQFzKij8T9KHADy/+t5T+xgPNu+Z56ncbS+gwjrDUsnvdob4Yuj+TY3NFZaXzx1aoVLqV2JiVYDevbtut7nBFR3wfvevLu9fBpUOGLKnetOr9ML3AzN0GGNhTBxud//v3U9ERBoPhWnBG9EBY29W/bpPh38zqu9CZrwzhZ2HkgAwVgieiI5nPN52pgCxpj38qyvcufWMM4DZjKYFk/rVsaMPu93Uedx0BCGMLu+G05y2+suKThBRCf9zoHpTMU4yGE2pCcGcYVSIMXaMacHJk2+gafFERJoOhWmhIi6d7365Pz1mAs2cJdz98l/x+SywheFuNeCMx/fwxBHhO23EUJET7hoOSXkwbX2t/cN9Dnp4NWZU6ldYGLRv72Pnzrrf5n7nupZ23haEGnvVkKgVHSF1H4TVnC4v19eZHcVvUFk5AGOc+HzN8fmaY0wYFRUjOH48G6+3U53rERGRhkNhWsDmpLjrcxjbqfmfW7f4hlm3PMCHu6/lxfX3UtT9r2CdeV3mn1d0xWed1hX30KCqhw/nvl0rbAD4LMOo8m4BvwyR70pK8rFrV93XE48klOUnbqaP+2JC98TC3pgaQzya+UKI9zbjnZOjaF+RSlHREgoLd1BUtICiokUUFOymuPgVfL6EOtciIiINi8K0AFAZn8nJXq/iieiAsUXgczTnjtSFpHTfxEOLZnPQm3bWY1uYMG4q70qYz1E1vOOvV8GkHOhbe9nkMJ+djPJEYrRwi5wHSUk+9u2zKC2t+7lamDDePDmSX/+r6uHBToMKSfS0YlDF5bxcNIythXfT09O6en+fLx63+39wu/thTMu6FyAiIg2SZvOQau6LBnG81cfYS3Zjq/wGn7MNj7/UmZQUB1OmwMsvn33hihmuAewoPcG2uzKqhnf84f/V2ifM56CzN4YnXQPr8zJEqiUl+TDGYs8eG716+T+jx+l2rGlBhw4+sltmwvGAnFJERBox9UxLTZaFNzIJd8y1eJt1on17w6RJlSxdGsLKlWf/c3kYDrpPvBXrYHNCX1lGROipMdgRJoQw42BkRVeWnhhNOCHn40pESEo6tax4IJSVwcaNdgYM+P6lxUVE5MKhnmn5QffeW8lbbzl4+OEw+vUrISqq9j7r19uZ/08n48ZVMqnjcN4u2c1uewEGQ2dvK0ZUJBFp6rByhogfLr/cR2io+e+46boH4JwcO+XlFtddpzAtIiJVFKblB4WGwp//XM7QoRHMmOFk5syKGu8XF8PEiWF07OjloYcqCCeU/1PeI0jVipzicECnToFZVhxg7VoHYWGG5OQzzKsuIiIXJA3zkHPSu7ePu+5y88orIfz73zU/No8+6uTwYYtnniknXM8VSgNTNaNH4ML0Ndd49TkXEZFqCtNyzqZMqaBNvI8HxxQR1bULrS6LZ3PK7/jnP0P59a/dXHVVYB7wEgmkLl18HDpko6iobuf56iuLvXttGuIhIiI1KEzLOYu0lfKc7T4+K2jDU/m34yq1c8/OiSRau5na/Y1glydyRklJVUMy6to7vW5d1ag4hWkRETmdwrScs7DX/klG4d/5OYv5v/yOW3mVg1zCPHM7cVPuA49ChjQ8iYlVfzHZvbtui7esW+fgsst8tG9/hrXCRUTkgqUwLecsbOF8rLIynmYCYZSzlOFM4in6sgncHhwfbwl2iSK1JCQYIiJMnXqmy8shO1tT4omISG0K03LOrMqqWTzacJRXGMtNvM7vmf7fNy0sd2UQqxM5M5ut7g8hfvSRndJSTYknIiK1KUzLOasYcgPG6QQgi7d4nZGEU7UqouVx4/7JlcEsT+SskpK87Nzp/+1u7VoHTqehXz9NiSciIjUpTMs5K/vFPZiIZhhbzY+NiYigZMIkaNYsSJWJfL+kJB/5+Tby8qwf3vkM1q2zk5zsJSIiwIWJiEijpzAt58zExnJ85Trc1/THhIRiwsPxtYzBNWU6ZRMfDHZ5Imf17bLiu3f/+FvegQMWe/ZovLSIiJyZVkCUH8V3eXtOvrkM68RxLJcLX5uLwV63WRJE6luXLlVhetcuG/37/7ihGqemxNMQDxERqU1hWvxioltiolsGuwyRcxIXZ4iO9m9Gj3Xr7LRr56NjRy1KJCIitWmYh4g0eZZV9RDijw3TlZXwwQcOUlM9WP4NtxYRkSZOYVpELghV0+PZMT9izZVNmzQlnoiIfD+FaRG5ICQl+Sgqsjhy5Ny7mNeudRAaan70OGsREblwKEyLyAXh9IcQz85NaOgSmje/mRYthrB+/VGSkwuJjNQS4iIicmYK0yJyQUhMrOpdPtviLQ7HJlq16kRU1L04ne9y7Nh+Pv20HenpT9Gy5TXYbEfPZ7kiItJIKEyLyAUhJgbi4nzs3l17Kke7fTstWmRisxVis7kAWLnyegCGDn0bu3030dEDsKwT57VmERFp+BSmReSCUfUQYu3bXmTkA1hWSY1tK1YMISHhAF27foZlebDZ8ggP/+v5KlVERBoJhWkRuWB06eJj924bvtOmjLbZviIkZGuNqe/cbgdr1gzk+utXVm+3rArCw/8C6GFEERE5RWFaRC4YSUk+ysos9u8/lZwdjm0YE1JjvyVLMikubs6QIStqbLesUiyr4LzUKiIijYPCtIhcMJKSqnqVd+06NW7ask51U/t8FjNnTmb06IV07fopaWmrap3j9P1FREQUpkXkgpGYWBWEP996CLtrNxiDx9Mdy/JQWNiSYcOWMmXKTEaOXMymTVfTrFlpjeONceLzXRSM0kVEpIFSmBaRC4K9eDttPxvEpRft58tN/6Flbgox2b2wH95NTk4WV175MatXD+K55+7ltdduITKy5gOJxoRSVvYLwBGcCxARkQZJ3woi0uQ5Tn5M9Jah4C2l+yU7+PTrRCxvKbbSffzjqc1Mmj+Piy8+RHZ2P3760821jjfGhjEtKCsbd/6LFxGRBk090yLS5EV9ejeWtxQL6J7wCbsOJ1HoasnoZxcyft6fGdzjPd5fs4nevXfi8zWrcazPF4nP15bjx1djjIZ4iIhITeqZFpEmzV68HXvZ19Wvu1/yCW5vKD0m7+DoiXhmjprMA8PnUHby9xQU7CEsbBFhYQuwrGJ8vospK/sllZWDgdqLvYiIiChMi0iT5ij5HGPZ+HYyvB4JOwDwGRvrHhlASpcPqvZzfQJEUl5+F+XldwWnWBERaXQUpkWkSTP2ZsCpeaV7ttvOGxNupF/nbOKjj1Xtgw3jaBGkCkVEpDFTmBaRJq0y5losc2rVQsuCG/u8WXMnWxgV8VnnuTIREWkK9ACiiDRt9maUtrsHY4s449vGCsUT1QNP8yvOc2EiItIUKEyLSJNX2vF3lLfOxNjCMf99kNAAPnsknsgunLxicXALFBGRRkvDPESk6bPsuLq/QNll4wn/+kXsJZ/jC42jvO3tuGNSqsZ+iIiI+EFhWkQuGN7Irri6zA52GSIi0oRomIeIiIiIiJ8UpkVERERE/KQwLSIiIiLiJ4VpERERERE/KUyLiIiIiPhJYVpERERExE8K0yIiIiIiflKYFhERERHxk8K0iIiIiIifFKZFRERERPykMC0iIiIi4ieFaRERERERPylMi4iIiIj4SWFaRERERMRPjmAX0BDYbFawS/hRGlu9jY3at36pfeuP2rZ+qX3rl9q3/jSWtm0sdX6XZYwxwS5CRERERKQx0jAPERERERE/KUyLiIiIiPhJYVpERERExE8K0yIiIiIiflKYFhERERHxk8K0iIiIiIifFKZFRERERPykMC0iIiIi4ieFaRERERERPylMNyJLly5l6NChpKWlMX/+/GCX0yS4XC5uuOEGDh48CMDGjRsZNmwYaWlpzJ49O8jVNW5z5swhPT2d9PR0nnjiCUDtG0hPP/00Q4cOJT09nblz5wJq30B7/PHHmTx5MgA7d+4kKyuLwYMH88gjj+DxeIJcXeN12223kZ6eTkZGBhkZGWzbtk3fbwG0bt06srKyGDJkCH/84x8B3RvqnZFG4ejRoyY1NdUcP37clJSUmGHDhpk9e/YEu6xGbevWreaGG24w3bp1M19//bUpKyszKSkp5sCBA8btdpuxY8ea999/P9hlNkrZ2dlm1KhRpqKiwlRWVpoxY8aYpUuXqn0DZNOmTWb06NHG7XabsrIyk5qaanbu3Kn2DaCNGzeaq6++2jz88MPGGGPS09PNf/7zH2OMMb/97W/N/Pnzg1leo+Xz+Uz//v2N2+2u3qbvt8A5cOCA6d+/vzly5IiprKw0N998s3n//fd1b6hn6pluJDZu3Ejfvn2Jjo4mIiKCwYMHs3LlymCX1agtXryY6dOnExcXB8D27du59NJLSUhIwOFwMGzYMLWxn2JjY5k8eTKhoaGEhITQoUMH9u3bp/YNkD59+vCPf/wDh8NBQUEBXq+XoqIitW+AnDhxgtmzZ3PPPfcAcOjQIcrLy/nJT34CQFZWltrWT3v37gVg7NixDB8+nFdffVXfbwG0evVqhg4dSnx8PCEhIcyePZvw8HDdG+qZwnQj8c033xAbG1v9Oi4ujmPHjgWxosbvT3/6E1dddVX1a7Vx4HTq1Kk6eOzbt48VK1ZgWZbaN4BCQkJ45plnSE9PJzk5WZ/fAJo2bRoTJ06kefPmQO17Q2xsrNrWT0VFRSQnJ/Pcc88xb948Fi5cyOHDh/XZDZD9+/fj9Xq55557yMjI4LXXXtO94TxQmG4kfD4flmVVvzbG1Hgtdac2Drw9e/YwduxYHnroIRISEtS+ATZ+/HhycnI4cuQI+/btU/sGwOuvv06bNm1ITk6u3qZ7Q+BcccUVPPHEE0RFRRETE8NNN93EM888o/YNEK/XS05ODjNmzGDRokVs376dr7/+Wu1bzxzBLkDOTXx8PJs3b65+nZeXVz08QQIjPj6evLy86tdq47rZsmUL48ePZ8qUKaSnp5Obm6v2DZAvv/ySyspKunTpQnh4OGlpaaxcuRK73V69j9rXP8uXLycvL4+MjAxOnjxJaWkplmXV+Ozm5+erbf20efNm3G539S8rxhjatm2re0OAXHTRRSQnJxMTEwPAwIEDdW84D9Qz3Uhcc8015OTkUFhYSFlZGatWreLaa68NdllNSq9evfjqq6+q/0y2bNkytbGfjhw5wrhx45g1axbp6emA2jeQDh48yNSpU6msrKSyspK1a9cyevRotW8AzJ07l2XLlvH2228zfvx4BgwYwMyZM3E6nWzZsgWAt99+W23rp+LiYp544gkqKipwuVy89dZbPPnkk/p+C5DU1FQ2bNhAUVERXq+XDz/8kOuvv173hnqmnulGonXr1kycOJExY8bgdru56aab6NmzZ7DLalKcTiePPfYY999/PxUVFaSkpHD99dcHu6xG6eWXX6aiooLHHnusetvo0aPVvgGSkpLC9u3byczMxG63k5aWRnp6OjExMWrfejJr1iymTp2Ky+WiW7dujBkzJtglNUqpqals27aNzMxMfD4ft9xyC71799b3W4D06tWLX/ziF9xyyy243W769evHzTffTPv27XVvqEeWMcYEuwgRERERkcZIwzxERERERPykMC0iIiIi4ieFaRERERERPylMi4iIiIj4SWFaRERERMRPmhpPROQ8e/bZZ5kzZ06NbZZlERYWRlxcHFdffTV33HEHHTp0CFKFIiJyrhSmRUSC5LrrrqNLly5A1ZLVLpeLXbt2sXjxYt555x2efvppfvaznwW3SBER+V4K0yIiQTJw4ECysrJqbV+/fj3jxo1j4sSJLFmyhEsvvTQI1YmIyLnQmGkRkQYmJSWFCRMmUFpayvPPPx/sckRE5HsoTIuINEC33norTqeTVatW4fF4AHC73fz9739n5MiR9O7dm+7du5Oamsq0adMoLCysPnbQoEH06tULl8tV67xz5swhMTGRjRs3ArB//34mTJhAamoq3bt3Z8CAATz66KPk5eWdnwsVEWnkFKZFRBqg8PBwunbtSmlpKTt37gRg0qRJzJgxA4fDwciRIxk1ahShoaEsWrSIX/7yl9XHZmRkUF5ezpo1a2qdd+nSpcTFxdG3b18KCwu54447WL9+PX369OHOO++kY8eOLFiwgDFjxuB2u8/b9YqINFYaMy0i0kC1bt0agLy8PLZu3cp7773HsGHDmDVrVvU+Ho+HESNG8Mknn/DVV19x+eWXk5GRwZw5c1i2bBmZmZnV+27fvp19+/Zx1113YbPZWL58OYcPH2bGjBnceOON1fv94Q9/YP78+WRnZ+sBSBGRH6CeaRGRBio0NBQAl8tFfHw8jz32GBMmTKixj8PhoHfv3gAUFBQAkJCQQO/evcnJyakx/OOdd94BqnquoWoGEYBt27bh9Xqr95s4cSIbNmxQkBYROQcK0yIiDVRJSQkAERERxMfHM2LECNq0acOnn37KsmXLeP7557n//vtZunQpcCocA2RmZuLxeFixYgUAXq+XFStWkJSURGJiIgCDBw8mOjqaRYsW0a9fPx544AHeeecdfD4fsbGx5/lqRUQaJ4VpEZEG6tChQ0BVTzPAwoULSU1NJSsri0mTJjF37lzKysqqF3cxxlQfO2TIEMLCwli2bBkA2dnZ5OfnV/dKQ9UwkjfeeIMbb7wRYwxLly7lwQcfpF+/fkybNo2KiorzdakiIo2WxkyLiDRAJ0+e5IsvvqB58+Z07NiRFStWMH36dBITE5k+fTrdunWjTZs2AEyfPp1t27bVOD4yMpLrrruO5cuXc+zYMVasWIHdbueGG26osV9CQgIzZszA6/XyySef8OGHH/Lmm2+yaNEioqKiePDBB8/bNYuINEbqmRYRaYAWLVqEx+NhyJAh2O326h7mp556ioEDB1YHaYC9e/cCNXumoWpstDGGtWvX8sEHH5CcnExcXFz1+2vXruXRRx/F5XJht9vp1asX9913H/Pnzwdgy5Yt9X2ZIiKNnsK0iEgDk5OTw3PPPUdERAR33303AE6nE4D8/Pwa+y5ZsoTc3FyA6vmov9W/f39iY2N56aWXag3xgKoQvmDBAhYsWFBj+7fDSy6++OLAXZSISBOlYR4iIkGyZs2a6uBqjKG4uJjPPvuMzZs3ExYWxuzZs2nbti0Aw4cP59133+W+++4jPT2dyMhIduzYQW5uLq1ataKgoIATJ07UOL/dbmfYsGG88sorREREMGjQoBrvjxw5ksWLFzNr1ixyc3NJTEykoKCAlStXEhERwa9+9avz0xAiIo2YZb77d0EREalXzz77LHPmzKm1PTw8nLZt29K3b19uv/122rVrV+P95cuX8+KLL7Jv3z7CwsJISEggIyODK664ghEjRjB8+HCefPLJGsds376dn//852RmZvL444/X+pnHjh3jL3/5C9nZ2Rw9epTIyEj69OnDfffdR6dOnQJ74SIiTZDCtIhIE7Zo0SKmTZvGvHnzSE5ODnY5IiJNjsK0iEgTVVxczKhRo3C73axatQrLsoJdkohIk6Mx0yIiTUxubi4zZszg4MGDFBcX8/jjjytIi4jUE83mISLSxMTFxZGfn4/dbmf8+PFkZmYGuyQRkSZLwzxERERERPyknmkRERERET8pTIuIiIiI+ElhWkRERETETwrTIiIiIiJ+UpgWEREREfGTwrSIiIiIiJ/+P12LtLzNDtztAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import cm\n",
    "import numpy as np\n",
    "from celluloid import Camera\n",
    "sns.set()\n",
    "numpoints = 10\n",
    "x=0\n",
    "y=1000\n",
    "points = np.array([[x],[y]])\n",
    "print(points.shape)\n",
    "colors = np.array(['red'])\n",
    "sizes = np.array([50])\n",
    "camera = Camera(plt.figure(figsize=(11,7)))\n",
    "plt.xlim(-5,65)\n",
    "plt.title('ETH-USDT',fontsize=20)\n",
    "plt.xlabel('Days',fontsize=20)\n",
    "plt.ylabel('USDT',fontsize=20)\n",
    "for i in range(50):\n",
    "    x=x+1\n",
    "    y=y+np.random.uniform(-15,15)\n",
    "    decission=np.random.uniform(0.3,0.7)\n",
    "    if decission < 0.4:\n",
    "        sizes=np.append(sizes,50)\n",
    "        colors=np.append(colors,'red')\n",
    "        annotation = plt.annotate(\"P(s) = \"+str(decission)[0:4]+\" NO\", xy=(x, y), weight=\"bold\", size=20)\n",
    "        y=y-5\n",
    "    elif decission < 0.5:\n",
    "        sizes=np.append(sizes,80)        \n",
    "        colors=np.append(colors,'orange')\n",
    "        annotation = plt.annotate(\"P(s) = \"+str(decission)[0:4]+\" Don't\", xy=(x, y), weight=\"bold\", size=20)\n",
    "        y=y-2\n",
    "    elif decission < 0.6:\n",
    "        sizes=np.append(sizes,110)\n",
    "        colors=np.append(colors,'yellow')\n",
    "        annotation = plt.annotate(\"P(s) = \"+str(decission)[0:4]+\" Could be\", xy=(x, y), weight=\"bold\", size=20)\n",
    "        y=y+3\n",
    "    else :\n",
    "        sizes=np.append(sizes,150)\n",
    "        colors=np.append(colors,'springgreen')\n",
    "        annotation = plt.annotate(\"P(s) = \"+str(decission)[0:4]+\" Go on!\", xy=(x, y), weight=\"bold\", size=20)\n",
    "        y=y+7\n",
    "    points=np.append(points,np.array([[x],[y]]),axis=1)\n",
    "    plt.scatter(*points, c=colors, s=sizes)\n",
    "    plt.plot(points[0],points[1],color='blue',lw=1.5)\n",
    "    camera.snap()\n",
    "anim = camera.animate(blit=True,interval=300)\n",
    "anim.save('scatter.gif')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.array([1,1]).reshape(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "points=np.append(points,np.array([[1],[1]]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "points[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.uniform(0,1,(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(10):\n",
    "    print(np.random.randint(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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